{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import environ\n",
    "\n",
    "environ['optimizer'] = 'Adam'\n",
    "environ['num_workers']= '2'\n",
    "environ['batch_size']= str(2048)\n",
    "environ['n_epochs']= '1000'\n",
    "environ['batch_norm']= 'True'\n",
    "environ['loss_func']='MAPE'\n",
    "environ['layers'] = '600 350 200 180'\n",
    "environ['dropouts'] = '0.1 '* 4\n",
    "environ['lr'] = '1e-03'\n",
    "environ['log'] = 'False'\n",
    "environ['weight_decay'] = '0.01'\n",
    "environ['cuda_device'] ='cuda:4'\n",
    "environ['dataset'] = 'data/speedup_dataset2.pkl'\n",
    "\n",
    "%run utils.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "function329_schedule_13\n",
      "0\n",
      "{'computations': {'computations_array': [{'comp_id': 1,\n",
      "                                          'lhs_data_type': 'p_int32',\n",
      "                                          'loop_iterators_ids': [2, 3],\n",
      "                                          'operations_histogram': [[5, 3, 0, 0],\n",
      "                                                                   [0, 0, 0, 0],\n",
      "                                                                   [0, 0, 0, 0],\n",
      "                                                                   [0, 0, 0, 0],\n",
      "                                                                   [0, 0, 0, 0],\n",
      "                                                                   [0, 0, 0, 0],\n",
      "                                                                   [0,\n",
      "                                                                    0,\n",
      "                                                                    0,\n",
      "                                                                    0]],\n",
      "                                          'rhs_accesses': {'accesses': [{'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     0],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     1]],\n",
      "                                                                         'comp_id': 0},\n",
      "                                                                        {'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     0],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     -1]],\n",
      "                                                                         'comp_id': 0},\n",
      "                                                                        {'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     1],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     0]],\n",
      "                                                                         'comp_id': 0},\n",
      "                                                                        {'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     1],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     1]],\n",
      "                                                                         'comp_id': 0},\n",
      "                                                                        {'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     1],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     -1]],\n",
      "                                                                         'comp_id': 0},\n",
      "                                                                        {'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     -1],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     0]],\n",
      "                                                                         'comp_id': 0},\n",
      "                                                                        {'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     -1],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     1]],\n",
      "                                                                         'comp_id': 0},\n",
      "                                                                        {'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     -1],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     -1]],\n",
      "                                                                         'comp_id': 0},\n",
      "                                                                        {'access': [[1,\n",
      "                                                                                     0,\n",
      "                                                                                     0],\n",
      "                                                                                    [0,\n",
      "                                                                                     1,\n",
      "                                                                                     0]],\n",
      "                                                                         'comp_id': 0}],\n",
      "                                                           'n': 9}}],\n",
      "                  'n': 1},\n",
      " 'inputs': {'inputs_array': [{'data_type': 'p_int32',\n",
      "                              'input_id': 0,\n",
      "                              'loop_iterators_ids': [0, 1]}],\n",
      "            'n': 1},\n",
      " 'iterators': {'iterators_array': [{'it_id': 2,\n",
      "                                    'lower_bound': 1,\n",
      "                                    'upper_bound': 1048575},\n",
      "                                   {'it_id': 3,\n",
      "                                    'lower_bound': 1,\n",
      "                                    'upper_bound': 63},\n",
      "                                   {'it_id': 0,\n",
      "                                    'lower_bound': 0,\n",
      "                                    'upper_bound': 1048576},\n",
      "                                   {'it_id': 1,\n",
      "                                    'lower_bound': 0,\n",
      "                                    'upper_bound': 64}],\n",
      "               'n': 4},\n",
      " 'loops': {'loops_array': [{'assignments': {'assignments_array': [], 'n': 0},\n",
      "                            'loop_id': 0,\n",
      "                            'loop_it': 2,\n",
      "                            'parent': -1,\n",
      "                            'position': 0},\n",
      "                           {'assignments': {'assignments_array': [{'id': 1,\n",
      "                                                                   'position': 0}],\n",
      "                                            'n': 1},\n",
      "                            'loop_id': 1,\n",
      "                            'loop_it': 3,\n",
      "                            'parent': 0,\n",
      "                            'position': 0}],\n",
      "           'n': 2},\n",
      " 'seed': 329,\n",
      " 'type': 2}\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'exit' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/data/scratch/henni-mohammed/speedup_model/src/data/loop_ast.py\u001b[0m in \u001b[0;36mtile\u001b[0;34m(self, loop_id, factor)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mloop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mloop_id\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    262\u001b[0m                 \u001b[0mloop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Computation' object has no attribute 'iterator'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-ccbb4c277821>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_dl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_dev_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_workers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfai\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasic_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataBunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_dl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-7-fa1393f8fd16>\u001b[0m in \u001b[0;36mtrain_dev_split\u001b[0;34m(dataset, batch_size, num_workers, log, seed)\u001b[0m\n\u001b[1;32m    108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    109\u001b[0m     \u001b[0mtest_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidation_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 110\u001b[0;31m     \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDatasetFromPkl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    111\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m     \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/scratch/henni-mohammed/speedup_model/src/data/dataset.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, filename, normalized, log, maxsize)\u001b[0m\n\u001b[1;32m    102\u001b[0m             \u001b[0mprogram\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprograms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogram_indexes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 104\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprogram\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_schedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschedules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/scratch/henni-mohammed/speedup_model/src/data/loop_ast.py\u001b[0m in \u001b[0;36madd_schedule\u001b[0;34m(self, schedule)\u001b[0m\n\u001b[1;32m    273\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0madd_schedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschedule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mLoop_AST\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdict_repr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschedule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    276\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdtype_to_int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/scratch/henni-mohammed/speedup_model/src/data/loop_ast.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, dict_repr, schedule)\u001b[0m\n\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_schedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/scratch/henni-mohammed/speedup_model/src/data/loop_ast.py\u001b[0m in \u001b[0;36mapply_schedule\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtype_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'tiling'\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mbinary_schedule\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mloop_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfactor\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfactors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloop_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfactor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mtype_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'interchange'\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mbinary_schedule\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/data/scratch/henni-mohammed/speedup_model/src/data/loop_ast.py\u001b[0m in \u001b[0;36mtile\u001b[0;34m(self, loop_id, factor)\u001b[0m\n\u001b[1;32m    269\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0mpprint\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpprint\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m             \u001b[0mpprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdict_repr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 271\u001b[0;31m             \u001b[0mexit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    272\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0madd_schedule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mschedule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'exit' is not defined"
     ]
    }
   ],
   "source": [
    "train_dl, val_dl, test_dl = train_dev_split(dataset, batch_size, num_workers, log=log)\n",
    "\n",
    "db = fai.basic_data.DataBunch(train_dl, val_dl, test_dl, device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size = train_dl.dataset.X.shape[1]\n",
    "output_size = train_dl.dataset.Y.shape[1]\n",
    "\n",
    "\n",
    "model = None \n",
    "\n",
    "if batch_norm:\n",
    "    model = Model_BN(input_size, output_size, hidden_sizes=layers_sizes, drops=drops)\n",
    "else:\n",
    "    model = Model(input_size, output_size)\n",
    "    \n",
    "if loss_func == 'MSE':\n",
    "    criterion = nn.MSELoss()\n",
    "elif loss_func == 'MAPE':\n",
    "    criterion = mape_criterion\n",
    "elif loss_func == 'SMAPE':\n",
    "    criterion = smape_criterion\n",
    "\n",
    "l = fai.Learner(db, model, loss_func=criterion, metrics=[mape_criterion, rmse_criterion])\n",
    "\n",
    "if optimizer == 'SGD':\n",
    "    l.opt_func = optim.SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = l.load(f\"r_speedup_{optimizer}_batch_norm_{batch_norm}_{loss_func}_nlayers_{len(layers_sizes)}_log_{log}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "l.lr_find()\n",
    "l.recorder.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 45:08 <p><table style='width:375px; margin-bottom:10px'>\n",
       "  <tr>\n",
       "    <th>epoch</th>\n",
       "    <th>train_loss</th>\n",
       "    <th>valid_loss</th>\n",
       "    <th>mape_criterion</th>\n",
       "    <th>rmse_criterion</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1</th>\n",
       "    <th>93.423729</th>\n",
       "    <th>92.886703</th>\n",
       "    <th>92.886703</th>\n",
       "    <th>2.151367</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>2</th>\n",
       "    <th>90.008125</th>\n",
       "    <th>90.564941</th>\n",
       "    <th>90.564941</th>\n",
       "    <th>2.121669</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>3</th>\n",
       "    <th>87.441612</th>\n",
       "    <th>90.928154</th>\n",
       "    <th>90.928154</th>\n",
       "    <th>2.121073</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>4</th>\n",
       "    <th>85.988487</th>\n",
       "    <th>82.488853</th>\n",
       "    <th>82.488853</th>\n",
       "    <th>2.109176</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>5</th>\n",
       "    <th>84.849869</th>\n",
       "    <th>84.743462</th>\n",
       "    <th>84.743462</th>\n",
       "    <th>2.109800</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>6</th>\n",
       "    <th>84.042099</th>\n",
       "    <th>80.569115</th>\n",
       "    <th>80.569115</th>\n",
       "    <th>2.104256</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>7</th>\n",
       "    <th>83.058922</th>\n",
       "    <th>78.487328</th>\n",
       "    <th>78.487328</th>\n",
       "    <th>2.091212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>8</th>\n",
       "    <th>81.217255</th>\n",
       "    <th>76.329285</th>\n",
       "    <th>76.329285</th>\n",
       "    <th>2.075416</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>9</th>\n",
       "    <th>79.180939</th>\n",
       "    <th>78.379677</th>\n",
       "    <th>78.379677</th>\n",
       "    <th>2.050333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>10</th>\n",
       "    <th>77.059280</th>\n",
       "    <th>71.022118</th>\n",
       "    <th>71.022118</th>\n",
       "    <th>2.021203</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>11</th>\n",
       "    <th>75.367386</th>\n",
       "    <th>71.858322</th>\n",
       "    <th>71.858322</th>\n",
       "    <th>1.986719</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>12</th>\n",
       "    <th>73.749138</th>\n",
       "    <th>71.017029</th>\n",
       "    <th>71.017029</th>\n",
       "    <th>1.965973</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>13</th>\n",
       "    <th>72.885902</th>\n",
       "    <th>69.800888</th>\n",
       "    <th>69.800888</th>\n",
       "    <th>1.956259</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>14</th>\n",
       "    <th>71.780472</th>\n",
       "    <th>67.621407</th>\n",
       "    <th>67.621407</th>\n",
       "    <th>1.948395</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>15</th>\n",
       "    <th>70.662575</th>\n",
       "    <th>67.442947</th>\n",
       "    <th>67.442947</th>\n",
       "    <th>1.937873</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>16</th>\n",
       "    <th>69.739517</th>\n",
       "    <th>68.700760</th>\n",
       "    <th>68.700760</th>\n",
       "    <th>1.912090</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>17</th>\n",
       "    <th>68.980522</th>\n",
       "    <th>65.830566</th>\n",
       "    <th>65.830566</th>\n",
       "    <th>1.915783</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>18</th>\n",
       "    <th>68.324333</th>\n",
       "    <th>66.291122</th>\n",
       "    <th>66.291122</th>\n",
       "    <th>1.917668</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>19</th>\n",
       "    <th>67.750145</th>\n",
       "    <th>65.657021</th>\n",
       "    <th>65.657021</th>\n",
       "    <th>1.905084</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>20</th>\n",
       "    <th>67.099663</th>\n",
       "    <th>67.751289</th>\n",
       "    <th>67.751289</th>\n",
       "    <th>1.904829</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>21</th>\n",
       "    <th>66.849792</th>\n",
       "    <th>64.616577</th>\n",
       "    <th>64.616577</th>\n",
       "    <th>1.902748</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>22</th>\n",
       "    <th>66.454506</th>\n",
       "    <th>65.658096</th>\n",
       "    <th>65.658096</th>\n",
       "    <th>1.905234</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>23</th>\n",
       "    <th>65.963043</th>\n",
       "    <th>64.889687</th>\n",
       "    <th>64.889687</th>\n",
       "    <th>1.900072</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>24</th>\n",
       "    <th>65.687309</th>\n",
       "    <th>64.256416</th>\n",
       "    <th>64.256416</th>\n",
       "    <th>1.900549</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>25</th>\n",
       "    <th>65.685730</th>\n",
       "    <th>63.896393</th>\n",
       "    <th>63.896393</th>\n",
       "    <th>1.900532</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>26</th>\n",
       "    <th>65.078636</th>\n",
       "    <th>66.024559</th>\n",
       "    <th>66.024559</th>\n",
       "    <th>1.890217</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>27</th>\n",
       "    <th>64.544525</th>\n",
       "    <th>63.466793</th>\n",
       "    <th>63.466793</th>\n",
       "    <th>1.891660</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>28</th>\n",
       "    <th>64.266411</th>\n",
       "    <th>63.767700</th>\n",
       "    <th>63.767700</th>\n",
       "    <th>1.898761</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>29</th>\n",
       "    <th>64.300644</th>\n",
       "    <th>64.126572</th>\n",
       "    <th>64.126572</th>\n",
       "    <th>1.893826</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>30</th>\n",
       "    <th>63.753143</th>\n",
       "    <th>63.824013</th>\n",
       "    <th>63.824013</th>\n",
       "    <th>1.886515</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>31</th>\n",
       "    <th>63.550941</th>\n",
       "    <th>62.455593</th>\n",
       "    <th>62.455593</th>\n",
       "    <th>1.898615</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>32</th>\n",
       "    <th>63.390476</th>\n",
       "    <th>63.994282</th>\n",
       "    <th>63.994282</th>\n",
       "    <th>1.878803</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>33</th>\n",
       "    <th>62.869576</th>\n",
       "    <th>63.678230</th>\n",
       "    <th>63.678230</th>\n",
       "    <th>1.870327</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>34</th>\n",
       "    <th>62.677452</th>\n",
       "    <th>63.201164</th>\n",
       "    <th>63.201164</th>\n",
       "    <th>1.878952</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>35</th>\n",
       "    <th>62.294273</th>\n",
       "    <th>61.948036</th>\n",
       "    <th>61.948036</th>\n",
       "    <th>1.887334</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>36</th>\n",
       "    <th>61.851658</th>\n",
       "    <th>61.683105</th>\n",
       "    <th>61.683105</th>\n",
       "    <th>1.879877</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>37</th>\n",
       "    <th>61.486454</th>\n",
       "    <th>62.984062</th>\n",
       "    <th>62.984062</th>\n",
       "    <th>1.875436</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>38</th>\n",
       "    <th>61.430180</th>\n",
       "    <th>62.450489</th>\n",
       "    <th>62.450489</th>\n",
       "    <th>1.879397</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>39</th>\n",
       "    <th>60.895878</th>\n",
       "    <th>61.473839</th>\n",
       "    <th>61.473839</th>\n",
       "    <th>1.870083</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>40</th>\n",
       "    <th>60.701664</th>\n",
       "    <th>61.794250</th>\n",
       "    <th>61.794250</th>\n",
       "    <th>1.863638</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>41</th>\n",
       "    <th>60.703831</th>\n",
       "    <th>59.990906</th>\n",
       "    <th>59.990906</th>\n",
       "    <th>1.869468</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>42</th>\n",
       "    <th>60.489658</th>\n",
       "    <th>61.760258</th>\n",
       "    <th>61.760258</th>\n",
       "    <th>1.871353</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>43</th>\n",
       "    <th>60.445705</th>\n",
       "    <th>62.018623</th>\n",
       "    <th>62.018623</th>\n",
       "    <th>1.869082</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>44</th>\n",
       "    <th>60.618343</th>\n",
       "    <th>60.619663</th>\n",
       "    <th>60.619663</th>\n",
       "    <th>1.869898</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>45</th>\n",
       "    <th>59.932102</th>\n",
       "    <th>60.571568</th>\n",
       "    <th>60.571568</th>\n",
       "    <th>1.873598</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>46</th>\n",
       "    <th>59.550003</th>\n",
       "    <th>60.300293</th>\n",
       "    <th>60.300293</th>\n",
       "    <th>1.859173</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>47</th>\n",
       "    <th>59.295273</th>\n",
       "    <th>60.590412</th>\n",
       "    <th>60.590412</th>\n",
       "    <th>1.866113</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>48</th>\n",
       "    <th>59.366318</th>\n",
       "    <th>61.007244</th>\n",
       "    <th>61.007244</th>\n",
       "    <th>1.862718</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>49</th>\n",
       "    <th>58.741428</th>\n",
       "    <th>59.827194</th>\n",
       "    <th>59.827194</th>\n",
       "    <th>1.864277</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>50</th>\n",
       "    <th>58.125751</th>\n",
       "    <th>58.521187</th>\n",
       "    <th>58.521187</th>\n",
       "    <th>1.862353</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>51</th>\n",
       "    <th>58.231216</th>\n",
       "    <th>60.600887</th>\n",
       "    <th>60.600887</th>\n",
       "    <th>1.860663</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>52</th>\n",
       "    <th>57.859852</th>\n",
       "    <th>58.356655</th>\n",
       "    <th>58.356655</th>\n",
       "    <th>1.846711</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>53</th>\n",
       "    <th>58.080448</th>\n",
       "    <th>59.311199</th>\n",
       "    <th>59.311199</th>\n",
       "    <th>1.839760</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>54</th>\n",
       "    <th>57.674160</th>\n",
       "    <th>59.118694</th>\n",
       "    <th>59.118694</th>\n",
       "    <th>1.845981</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>55</th>\n",
       "    <th>57.989952</th>\n",
       "    <th>58.762867</th>\n",
       "    <th>58.762867</th>\n",
       "    <th>1.844624</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>56</th>\n",
       "    <th>57.189388</th>\n",
       "    <th>59.739880</th>\n",
       "    <th>59.739880</th>\n",
       "    <th>1.847841</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>57</th>\n",
       "    <th>56.805458</th>\n",
       "    <th>57.344543</th>\n",
       "    <th>57.344543</th>\n",
       "    <th>1.827263</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>58</th>\n",
       "    <th>56.927792</th>\n",
       "    <th>57.210911</th>\n",
       "    <th>57.210911</th>\n",
       "    <th>1.838151</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>59</th>\n",
       "    <th>56.446651</th>\n",
       "    <th>56.911793</th>\n",
       "    <th>56.911793</th>\n",
       "    <th>1.828753</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>60</th>\n",
       "    <th>57.314789</th>\n",
       "    <th>57.642555</th>\n",
       "    <th>57.642555</th>\n",
       "    <th>1.845361</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>61</th>\n",
       "    <th>56.211998</th>\n",
       "    <th>57.082108</th>\n",
       "    <th>57.082108</th>\n",
       "    <th>1.842204</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>62</th>\n",
       "    <th>56.541008</th>\n",
       "    <th>57.812870</th>\n",
       "    <th>57.812870</th>\n",
       "    <th>1.848601</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>63</th>\n",
       "    <th>56.269676</th>\n",
       "    <th>57.311989</th>\n",
       "    <th>57.311989</th>\n",
       "    <th>1.841315</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>64</th>\n",
       "    <th>55.498844</th>\n",
       "    <th>55.991177</th>\n",
       "    <th>55.991177</th>\n",
       "    <th>1.830838</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>65</th>\n",
       "    <th>54.654984</th>\n",
       "    <th>55.505642</th>\n",
       "    <th>55.505642</th>\n",
       "    <th>1.808162</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>66</th>\n",
       "    <th>54.469208</th>\n",
       "    <th>57.453400</th>\n",
       "    <th>57.453400</th>\n",
       "    <th>1.824829</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>67</th>\n",
       "    <th>55.756466</th>\n",
       "    <th>58.340237</th>\n",
       "    <th>58.340237</th>\n",
       "    <th>1.816156</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>68</th>\n",
       "    <th>53.878979</th>\n",
       "    <th>54.004669</th>\n",
       "    <th>54.004669</th>\n",
       "    <th>1.804037</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>69</th>\n",
       "    <th>53.915031</th>\n",
       "    <th>53.176514</th>\n",
       "    <th>53.176514</th>\n",
       "    <th>1.815466</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>70</th>\n",
       "    <th>54.387188</th>\n",
       "    <th>53.640770</th>\n",
       "    <th>53.640770</th>\n",
       "    <th>1.796725</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>71</th>\n",
       "    <th>54.520706</th>\n",
       "    <th>54.757725</th>\n",
       "    <th>54.757725</th>\n",
       "    <th>1.817876</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>72</th>\n",
       "    <th>54.440834</th>\n",
       "    <th>55.750206</th>\n",
       "    <th>55.750206</th>\n",
       "    <th>1.827223</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>73</th>\n",
       "    <th>52.921066</th>\n",
       "    <th>53.862232</th>\n",
       "    <th>53.862232</th>\n",
       "    <th>1.813870</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>74</th>\n",
       "    <th>51.912167</th>\n",
       "    <th>53.865524</th>\n",
       "    <th>53.865524</th>\n",
       "    <th>1.801448</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>75</th>\n",
       "    <th>52.851559</th>\n",
       "    <th>55.339523</th>\n",
       "    <th>55.339523</th>\n",
       "    <th>1.826535</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>76</th>\n",
       "    <th>52.225471</th>\n",
       "    <th>53.286713</th>\n",
       "    <th>53.286713</th>\n",
       "    <th>1.820012</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>77</th>\n",
       "    <th>53.613449</th>\n",
       "    <th>55.083450</th>\n",
       "    <th>55.083450</th>\n",
       "    <th>1.815953</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>78</th>\n",
       "    <th>52.415043</th>\n",
       "    <th>54.980469</th>\n",
       "    <th>54.980469</th>\n",
       "    <th>1.812408</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>79</th>\n",
       "    <th>51.986000</th>\n",
       "    <th>51.744564</th>\n",
       "    <th>51.744564</th>\n",
       "    <th>1.799595</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>80</th>\n",
       "    <th>50.847519</th>\n",
       "    <th>54.298313</th>\n",
       "    <th>54.298313</th>\n",
       "    <th>1.793131</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>81</th>\n",
       "    <th>51.508739</th>\n",
       "    <th>53.109951</th>\n",
       "    <th>53.109951</th>\n",
       "    <th>1.788502</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>82</th>\n",
       "    <th>50.698505</th>\n",
       "    <th>54.083305</th>\n",
       "    <th>54.083305</th>\n",
       "    <th>1.795928</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>83</th>\n",
       "    <th>49.459572</th>\n",
       "    <th>52.487263</th>\n",
       "    <th>52.487263</th>\n",
       "    <th>1.810230</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>84</th>\n",
       "    <th>49.645176</th>\n",
       "    <th>52.614468</th>\n",
       "    <th>52.614468</th>\n",
       "    <th>1.779876</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>85</th>\n",
       "    <th>49.944866</th>\n",
       "    <th>51.765957</th>\n",
       "    <th>51.765957</th>\n",
       "    <th>1.806614</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>86</th>\n",
       "    <th>49.436363</th>\n",
       "    <th>50.250168</th>\n",
       "    <th>50.250168</th>\n",
       "    <th>1.789255</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>87</th>\n",
       "    <th>48.894447</th>\n",
       "    <th>53.052895</th>\n",
       "    <th>53.052895</th>\n",
       "    <th>1.778217</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>88</th>\n",
       "    <th>48.024010</th>\n",
       "    <th>50.191875</th>\n",
       "    <th>50.191875</th>\n",
       "    <th>1.763596</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>89</th>\n",
       "    <th>47.054611</th>\n",
       "    <th>48.424953</th>\n",
       "    <th>48.424953</th>\n",
       "    <th>1.765152</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>90</th>\n",
       "    <th>47.827393</th>\n",
       "    <th>49.173283</th>\n",
       "    <th>49.173283</th>\n",
       "    <th>1.774695</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>91</th>\n",
       "    <th>46.416439</th>\n",
       "    <th>47.108303</th>\n",
       "    <th>47.108303</th>\n",
       "    <th>1.760648</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>92</th>\n",
       "    <th>47.977962</th>\n",
       "    <th>52.559898</th>\n",
       "    <th>52.559898</th>\n",
       "    <th>1.764530</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>93</th>\n",
       "    <th>46.690086</th>\n",
       "    <th>50.352188</th>\n",
       "    <th>50.352188</th>\n",
       "    <th>1.770443</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>94</th>\n",
       "    <th>45.861080</th>\n",
       "    <th>46.693974</th>\n",
       "    <th>46.693974</th>\n",
       "    <th>1.747919</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>95</th>\n",
       "    <th>45.097187</th>\n",
       "    <th>46.199917</th>\n",
       "    <th>46.199917</th>\n",
       "    <th>1.745117</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>96</th>\n",
       "    <th>44.542435</th>\n",
       "    <th>44.678600</th>\n",
       "    <th>44.678600</th>\n",
       "    <th>1.743595</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>97</th>\n",
       "    <th>44.209557</th>\n",
       "    <th>46.577957</th>\n",
       "    <th>46.577957</th>\n",
       "    <th>1.740955</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>98</th>\n",
       "    <th>43.707783</th>\n",
       "    <th>49.595966</th>\n",
       "    <th>49.595966</th>\n",
       "    <th>1.718057</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>99</th>\n",
       "    <th>43.942543</th>\n",
       "    <th>45.852474</th>\n",
       "    <th>45.852474</th>\n",
       "    <th>1.730177</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>100</th>\n",
       "    <th>42.688175</th>\n",
       "    <th>45.043205</th>\n",
       "    <th>45.043205</th>\n",
       "    <th>1.724928</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>101</th>\n",
       "    <th>42.229996</th>\n",
       "    <th>44.247334</th>\n",
       "    <th>44.247334</th>\n",
       "    <th>1.712397</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>102</th>\n",
       "    <th>43.504009</th>\n",
       "    <th>49.293007</th>\n",
       "    <th>49.293007</th>\n",
       "    <th>1.710653</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>103</th>\n",
       "    <th>41.612804</th>\n",
       "    <th>46.086716</th>\n",
       "    <th>46.086716</th>\n",
       "    <th>1.699165</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>104</th>\n",
       "    <th>40.751049</th>\n",
       "    <th>44.134087</th>\n",
       "    <th>44.134087</th>\n",
       "    <th>1.685948</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>105</th>\n",
       "    <th>39.959339</th>\n",
       "    <th>42.537525</th>\n",
       "    <th>42.537525</th>\n",
       "    <th>1.653144</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>106</th>\n",
       "    <th>39.630096</th>\n",
       "    <th>41.711414</th>\n",
       "    <th>41.711414</th>\n",
       "    <th>1.656723</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>107</th>\n",
       "    <th>39.448368</th>\n",
       "    <th>42.894669</th>\n",
       "    <th>42.894669</th>\n",
       "    <th>1.626352</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>108</th>\n",
       "    <th>39.217857</th>\n",
       "    <th>40.008839</th>\n",
       "    <th>40.008839</th>\n",
       "    <th>1.617849</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>109</th>\n",
       "    <th>37.500298</th>\n",
       "    <th>41.144558</th>\n",
       "    <th>41.144558</th>\n",
       "    <th>1.574913</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>110</th>\n",
       "    <th>38.143944</th>\n",
       "    <th>42.648933</th>\n",
       "    <th>42.648933</th>\n",
       "    <th>1.580345</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>111</th>\n",
       "    <th>38.013390</th>\n",
       "    <th>42.361748</th>\n",
       "    <th>42.361748</th>\n",
       "    <th>1.594960</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>112</th>\n",
       "    <th>37.394905</th>\n",
       "    <th>39.193226</th>\n",
       "    <th>39.193226</th>\n",
       "    <th>1.571904</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>113</th>\n",
       "    <th>36.521023</th>\n",
       "    <th>39.822884</th>\n",
       "    <th>39.822884</th>\n",
       "    <th>1.480904</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>114</th>\n",
       "    <th>36.648323</th>\n",
       "    <th>38.904179</th>\n",
       "    <th>38.904179</th>\n",
       "    <th>1.542928</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>115</th>\n",
       "    <th>36.038670</th>\n",
       "    <th>38.908005</th>\n",
       "    <th>38.908005</th>\n",
       "    <th>1.491711</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>116</th>\n",
       "    <th>35.450012</th>\n",
       "    <th>38.244247</th>\n",
       "    <th>38.244247</th>\n",
       "    <th>1.480929</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>117</th>\n",
       "    <th>36.912319</th>\n",
       "    <th>38.740875</th>\n",
       "    <th>38.740875</th>\n",
       "    <th>1.516708</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>118</th>\n",
       "    <th>34.309265</th>\n",
       "    <th>37.862049</th>\n",
       "    <th>37.862049</th>\n",
       "    <th>1.517176</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>119</th>\n",
       "    <th>36.295097</th>\n",
       "    <th>41.259937</th>\n",
       "    <th>41.259937</th>\n",
       "    <th>1.509100</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>120</th>\n",
       "    <th>35.909367</th>\n",
       "    <th>39.890354</th>\n",
       "    <th>39.890354</th>\n",
       "    <th>1.577595</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>121</th>\n",
       "    <th>35.651096</th>\n",
       "    <th>39.204170</th>\n",
       "    <th>39.204170</th>\n",
       "    <th>1.422837</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>122</th>\n",
       "    <th>34.990543</th>\n",
       "    <th>40.432850</th>\n",
       "    <th>40.432850</th>\n",
       "    <th>1.555114</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>123</th>\n",
       "    <th>34.354893</th>\n",
       "    <th>42.599201</th>\n",
       "    <th>42.599201</th>\n",
       "    <th>1.624999</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>124</th>\n",
       "    <th>34.985119</th>\n",
       "    <th>40.072464</th>\n",
       "    <th>40.072464</th>\n",
       "    <th>1.475487</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>125</th>\n",
       "    <th>35.478622</th>\n",
       "    <th>42.687031</th>\n",
       "    <th>42.687031</th>\n",
       "    <th>1.442627</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>126</th>\n",
       "    <th>33.720520</th>\n",
       "    <th>39.828445</th>\n",
       "    <th>39.828445</th>\n",
       "    <th>1.409073</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>127</th>\n",
       "    <th>35.498226</th>\n",
       "    <th>39.983025</th>\n",
       "    <th>39.983025</th>\n",
       "    <th>1.455086</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>128</th>\n",
       "    <th>33.431789</th>\n",
       "    <th>37.160717</th>\n",
       "    <th>37.160717</th>\n",
       "    <th>1.276967</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>129</th>\n",
       "    <th>32.926098</th>\n",
       "    <th>36.939423</th>\n",
       "    <th>36.939423</th>\n",
       "    <th>1.276082</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>130</th>\n",
       "    <th>33.597218</th>\n",
       "    <th>39.606991</th>\n",
       "    <th>39.606991</th>\n",
       "    <th>1.355963</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>131</th>\n",
       "    <th>34.437305</th>\n",
       "    <th>37.275333</th>\n",
       "    <th>37.275333</th>\n",
       "    <th>1.342840</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>132</th>\n",
       "    <th>33.127857</th>\n",
       "    <th>36.900997</th>\n",
       "    <th>36.900997</th>\n",
       "    <th>1.331129</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>133</th>\n",
       "    <th>32.778633</th>\n",
       "    <th>38.095993</th>\n",
       "    <th>38.095993</th>\n",
       "    <th>1.238150</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>134</th>\n",
       "    <th>33.084644</th>\n",
       "    <th>39.056728</th>\n",
       "    <th>39.056728</th>\n",
       "    <th>1.292956</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>135</th>\n",
       "    <th>33.625137</th>\n",
       "    <th>36.012817</th>\n",
       "    <th>36.012817</th>\n",
       "    <th>1.272156</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>136</th>\n",
       "    <th>32.444103</th>\n",
       "    <th>37.841064</th>\n",
       "    <th>37.841064</th>\n",
       "    <th>1.315038</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>137</th>\n",
       "    <th>31.788614</th>\n",
       "    <th>36.168797</th>\n",
       "    <th>36.168797</th>\n",
       "    <th>1.203314</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>138</th>\n",
       "    <th>33.218967</th>\n",
       "    <th>44.705338</th>\n",
       "    <th>44.705338</th>\n",
       "    <th>1.479144</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>139</th>\n",
       "    <th>31.906897</th>\n",
       "    <th>36.557972</th>\n",
       "    <th>36.557972</th>\n",
       "    <th>1.230343</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>140</th>\n",
       "    <th>31.002033</th>\n",
       "    <th>36.387039</th>\n",
       "    <th>36.387039</th>\n",
       "    <th>1.181952</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>141</th>\n",
       "    <th>31.098389</th>\n",
       "    <th>37.193787</th>\n",
       "    <th>37.193787</th>\n",
       "    <th>1.167181</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>142</th>\n",
       "    <th>32.132996</th>\n",
       "    <th>38.194859</th>\n",
       "    <th>38.194859</th>\n",
       "    <th>1.295227</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>143</th>\n",
       "    <th>30.813057</th>\n",
       "    <th>36.395706</th>\n",
       "    <th>36.395706</th>\n",
       "    <th>1.234051</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>144</th>\n",
       "    <th>30.909916</th>\n",
       "    <th>36.813267</th>\n",
       "    <th>36.813267</th>\n",
       "    <th>1.259240</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>145</th>\n",
       "    <th>31.046415</th>\n",
       "    <th>36.432796</th>\n",
       "    <th>36.432796</th>\n",
       "    <th>1.240296</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>146</th>\n",
       "    <th>31.110601</th>\n",
       "    <th>36.687782</th>\n",
       "    <th>36.687782</th>\n",
       "    <th>1.248238</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>147</th>\n",
       "    <th>30.722334</th>\n",
       "    <th>34.282196</th>\n",
       "    <th>34.282196</th>\n",
       "    <th>1.087705</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>148</th>\n",
       "    <th>30.371082</th>\n",
       "    <th>36.195553</th>\n",
       "    <th>36.195553</th>\n",
       "    <th>1.162783</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>149</th>\n",
       "    <th>30.634193</th>\n",
       "    <th>40.876049</th>\n",
       "    <th>40.876049</th>\n",
       "    <th>1.330243</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>150</th>\n",
       "    <th>31.168163</th>\n",
       "    <th>36.248688</th>\n",
       "    <th>36.248688</th>\n",
       "    <th>1.225981</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>151</th>\n",
       "    <th>31.421040</th>\n",
       "    <th>39.345921</th>\n",
       "    <th>39.345921</th>\n",
       "    <th>1.346880</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>152</th>\n",
       "    <th>31.550833</th>\n",
       "    <th>38.526199</th>\n",
       "    <th>38.526199</th>\n",
       "    <th>1.435257</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>153</th>\n",
       "    <th>30.880020</th>\n",
       "    <th>34.927494</th>\n",
       "    <th>34.927494</th>\n",
       "    <th>1.143413</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>154</th>\n",
       "    <th>30.101904</th>\n",
       "    <th>38.139950</th>\n",
       "    <th>38.139950</th>\n",
       "    <th>1.167680</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>155</th>\n",
       "    <th>29.408663</th>\n",
       "    <th>36.377659</th>\n",
       "    <th>36.377659</th>\n",
       "    <th>1.153711</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>156</th>\n",
       "    <th>32.200687</th>\n",
       "    <th>41.684929</th>\n",
       "    <th>41.684929</th>\n",
       "    <th>1.361820</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>157</th>\n",
       "    <th>31.670715</th>\n",
       "    <th>38.642654</th>\n",
       "    <th>38.642654</th>\n",
       "    <th>1.270820</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>158</th>\n",
       "    <th>30.200882</th>\n",
       "    <th>35.924175</th>\n",
       "    <th>35.924175</th>\n",
       "    <th>1.117240</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>159</th>\n",
       "    <th>30.926067</th>\n",
       "    <th>35.674110</th>\n",
       "    <th>35.674110</th>\n",
       "    <th>1.296241</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>160</th>\n",
       "    <th>30.411051</th>\n",
       "    <th>36.012215</th>\n",
       "    <th>36.012215</th>\n",
       "    <th>1.168657</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>161</th>\n",
       "    <th>29.359867</th>\n",
       "    <th>37.577206</th>\n",
       "    <th>37.577206</th>\n",
       "    <th>1.225620</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>162</th>\n",
       "    <th>29.248468</th>\n",
       "    <th>40.970772</th>\n",
       "    <th>40.970772</th>\n",
       "    <th>1.053959</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>163</th>\n",
       "    <th>28.901318</th>\n",
       "    <th>33.781521</th>\n",
       "    <th>33.781521</th>\n",
       "    <th>1.062111</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>164</th>\n",
       "    <th>28.376612</th>\n",
       "    <th>36.286755</th>\n",
       "    <th>36.286755</th>\n",
       "    <th>1.092795</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>165</th>\n",
       "    <th>29.314718</th>\n",
       "    <th>36.708366</th>\n",
       "    <th>36.708366</th>\n",
       "    <th>1.289292</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>166</th>\n",
       "    <th>30.815804</th>\n",
       "    <th>37.570915</th>\n",
       "    <th>37.570915</th>\n",
       "    <th>1.287095</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>167</th>\n",
       "    <th>29.344730</th>\n",
       "    <th>34.326561</th>\n",
       "    <th>34.326561</th>\n",
       "    <th>1.090107</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>168</th>\n",
       "    <th>28.954918</th>\n",
       "    <th>34.411575</th>\n",
       "    <th>34.411575</th>\n",
       "    <th>1.026488</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>169</th>\n",
       "    <th>28.427608</th>\n",
       "    <th>35.874065</th>\n",
       "    <th>35.874065</th>\n",
       "    <th>1.086574</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>170</th>\n",
       "    <th>28.995028</th>\n",
       "    <th>37.635605</th>\n",
       "    <th>37.635605</th>\n",
       "    <th>1.087841</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>171</th>\n",
       "    <th>28.385517</th>\n",
       "    <th>36.574280</th>\n",
       "    <th>36.574280</th>\n",
       "    <th>1.232468</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>172</th>\n",
       "    <th>28.290279</th>\n",
       "    <th>35.523605</th>\n",
       "    <th>35.523605</th>\n",
       "    <th>1.099374</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>173</th>\n",
       "    <th>31.197781</th>\n",
       "    <th>39.945423</th>\n",
       "    <th>39.945423</th>\n",
       "    <th>1.334375</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>174</th>\n",
       "    <th>29.142756</th>\n",
       "    <th>33.905937</th>\n",
       "    <th>33.905937</th>\n",
       "    <th>1.052841</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>175</th>\n",
       "    <th>28.890253</th>\n",
       "    <th>35.790375</th>\n",
       "    <th>35.790375</th>\n",
       "    <th>1.192218</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>176</th>\n",
       "    <th>28.611275</th>\n",
       "    <th>37.254780</th>\n",
       "    <th>37.254780</th>\n",
       "    <th>1.286893</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>177</th>\n",
       "    <th>28.169529</th>\n",
       "    <th>36.661125</th>\n",
       "    <th>36.661125</th>\n",
       "    <th>1.111457</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>178</th>\n",
       "    <th>29.074125</th>\n",
       "    <th>36.789036</th>\n",
       "    <th>36.789036</th>\n",
       "    <th>1.243622</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>179</th>\n",
       "    <th>28.903521</th>\n",
       "    <th>37.098026</th>\n",
       "    <th>37.098026</th>\n",
       "    <th>1.192256</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>180</th>\n",
       "    <th>28.088675</th>\n",
       "    <th>36.068771</th>\n",
       "    <th>36.068771</th>\n",
       "    <th>1.123754</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>181</th>\n",
       "    <th>28.248381</th>\n",
       "    <th>32.929607</th>\n",
       "    <th>32.929607</th>\n",
       "    <th>0.994405</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>182</th>\n",
       "    <th>28.492708</th>\n",
       "    <th>36.671429</th>\n",
       "    <th>36.671429</th>\n",
       "    <th>1.145542</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>183</th>\n",
       "    <th>29.715656</th>\n",
       "    <th>36.924007</th>\n",
       "    <th>36.924007</th>\n",
       "    <th>1.197908</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>184</th>\n",
       "    <th>29.766714</th>\n",
       "    <th>32.829159</th>\n",
       "    <th>32.829159</th>\n",
       "    <th>0.982243</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>185</th>\n",
       "    <th>28.746042</th>\n",
       "    <th>34.658680</th>\n",
       "    <th>34.658680</th>\n",
       "    <th>1.073998</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>186</th>\n",
       "    <th>28.496502</th>\n",
       "    <th>33.912632</th>\n",
       "    <th>33.912632</th>\n",
       "    <th>1.090034</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>187</th>\n",
       "    <th>28.866394</th>\n",
       "    <th>36.834583</th>\n",
       "    <th>36.834583</th>\n",
       "    <th>1.131060</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>188</th>\n",
       "    <th>29.339457</th>\n",
       "    <th>36.071270</th>\n",
       "    <th>36.071270</th>\n",
       "    <th>1.145940</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>189</th>\n",
       "    <th>28.755386</th>\n",
       "    <th>34.433186</th>\n",
       "    <th>34.433186</th>\n",
       "    <th>1.085106</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>190</th>\n",
       "    <th>28.251207</th>\n",
       "    <th>34.446064</th>\n",
       "    <th>34.446064</th>\n",
       "    <th>1.054435</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>191</th>\n",
       "    <th>28.381437</th>\n",
       "    <th>35.983681</th>\n",
       "    <th>35.983681</th>\n",
       "    <th>1.109422</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>192</th>\n",
       "    <th>27.564554</th>\n",
       "    <th>34.483917</th>\n",
       "    <th>34.483917</th>\n",
       "    <th>1.115098</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>193</th>\n",
       "    <th>26.570990</th>\n",
       "    <th>36.204716</th>\n",
       "    <th>36.204716</th>\n",
       "    <th>1.115065</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>194</th>\n",
       "    <th>27.457443</th>\n",
       "    <th>36.109566</th>\n",
       "    <th>36.109566</th>\n",
       "    <th>1.217181</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>195</th>\n",
       "    <th>27.436111</th>\n",
       "    <th>35.149189</th>\n",
       "    <th>35.149189</th>\n",
       "    <th>1.153856</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>196</th>\n",
       "    <th>28.179209</th>\n",
       "    <th>36.511330</th>\n",
       "    <th>36.511330</th>\n",
       "    <th>1.224901</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>197</th>\n",
       "    <th>26.639977</th>\n",
       "    <th>35.283924</th>\n",
       "    <th>35.283924</th>\n",
       "    <th>1.111858</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>198</th>\n",
       "    <th>27.400696</th>\n",
       "    <th>36.344658</th>\n",
       "    <th>36.344658</th>\n",
       "    <th>1.314768</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>199</th>\n",
       "    <th>29.826334</th>\n",
       "    <th>37.384705</th>\n",
       "    <th>37.384705</th>\n",
       "    <th>1.265070</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>200</th>\n",
       "    <th>27.377468</th>\n",
       "    <th>34.942219</th>\n",
       "    <th>34.942219</th>\n",
       "    <th>1.031972</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>201</th>\n",
       "    <th>27.258446</th>\n",
       "    <th>34.488743</th>\n",
       "    <th>34.488743</th>\n",
       "    <th>1.040999</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>202</th>\n",
       "    <th>28.341686</th>\n",
       "    <th>34.802811</th>\n",
       "    <th>34.802811</th>\n",
       "    <th>1.137810</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>203</th>\n",
       "    <th>28.513752</th>\n",
       "    <th>32.987247</th>\n",
       "    <th>32.987247</th>\n",
       "    <th>0.966048</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>204</th>\n",
       "    <th>26.902483</th>\n",
       "    <th>34.176987</th>\n",
       "    <th>34.176987</th>\n",
       "    <th>1.032551</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>205</th>\n",
       "    <th>27.038607</th>\n",
       "    <th>35.054035</th>\n",
       "    <th>35.054035</th>\n",
       "    <th>1.074166</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>206</th>\n",
       "    <th>28.963821</th>\n",
       "    <th>34.271477</th>\n",
       "    <th>34.271477</th>\n",
       "    <th>1.057474</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>207</th>\n",
       "    <th>28.175613</th>\n",
       "    <th>36.744225</th>\n",
       "    <th>36.744225</th>\n",
       "    <th>1.054023</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>208</th>\n",
       "    <th>27.422148</th>\n",
       "    <th>33.423111</th>\n",
       "    <th>33.423111</th>\n",
       "    <th>0.984831</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>209</th>\n",
       "    <th>26.879574</th>\n",
       "    <th>33.204124</th>\n",
       "    <th>33.204124</th>\n",
       "    <th>1.000941</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>210</th>\n",
       "    <th>29.344530</th>\n",
       "    <th>36.138439</th>\n",
       "    <th>36.138439</th>\n",
       "    <th>1.256953</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>211</th>\n",
       "    <th>27.193022</th>\n",
       "    <th>33.705208</th>\n",
       "    <th>33.705208</th>\n",
       "    <th>0.978992</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>212</th>\n",
       "    <th>26.574984</th>\n",
       "    <th>36.295052</th>\n",
       "    <th>36.295052</th>\n",
       "    <th>1.012068</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>213</th>\n",
       "    <th>28.766533</th>\n",
       "    <th>39.487171</th>\n",
       "    <th>39.487171</th>\n",
       "    <th>1.317726</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>214</th>\n",
       "    <th>27.124079</th>\n",
       "    <th>35.049259</th>\n",
       "    <th>35.049259</th>\n",
       "    <th>1.142368</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>215</th>\n",
       "    <th>27.472672</th>\n",
       "    <th>34.372227</th>\n",
       "    <th>34.372227</th>\n",
       "    <th>1.020166</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>216</th>\n",
       "    <th>26.355528</th>\n",
       "    <th>33.359673</th>\n",
       "    <th>33.359673</th>\n",
       "    <th>1.034296</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>217</th>\n",
       "    <th>26.775797</th>\n",
       "    <th>35.085617</th>\n",
       "    <th>35.085617</th>\n",
       "    <th>1.136977</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>218</th>\n",
       "    <th>28.232878</th>\n",
       "    <th>35.001961</th>\n",
       "    <th>35.001961</th>\n",
       "    <th>1.117813</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>219</th>\n",
       "    <th>28.054819</th>\n",
       "    <th>33.653423</th>\n",
       "    <th>33.653423</th>\n",
       "    <th>0.956515</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>220</th>\n",
       "    <th>27.306807</th>\n",
       "    <th>33.610516</th>\n",
       "    <th>33.610516</th>\n",
       "    <th>0.988703</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>221</th>\n",
       "    <th>26.701019</th>\n",
       "    <th>33.753139</th>\n",
       "    <th>33.753139</th>\n",
       "    <th>0.934315</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>222</th>\n",
       "    <th>28.513618</th>\n",
       "    <th>36.572571</th>\n",
       "    <th>36.572571</th>\n",
       "    <th>1.108264</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>223</th>\n",
       "    <th>28.175514</th>\n",
       "    <th>39.238861</th>\n",
       "    <th>39.238861</th>\n",
       "    <th>1.140818</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>224</th>\n",
       "    <th>26.749100</th>\n",
       "    <th>34.845196</th>\n",
       "    <th>34.845196</th>\n",
       "    <th>1.040005</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>225</th>\n",
       "    <th>28.133591</th>\n",
       "    <th>35.002987</th>\n",
       "    <th>35.002987</th>\n",
       "    <th>1.021567</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>226</th>\n",
       "    <th>27.908960</th>\n",
       "    <th>33.464874</th>\n",
       "    <th>33.464874</th>\n",
       "    <th>1.052810</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>227</th>\n",
       "    <th>26.296911</th>\n",
       "    <th>33.462784</th>\n",
       "    <th>33.462784</th>\n",
       "    <th>0.979090</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>228</th>\n",
       "    <th>26.119671</th>\n",
       "    <th>32.817814</th>\n",
       "    <th>32.817814</th>\n",
       "    <th>0.981565</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>229</th>\n",
       "    <th>25.988014</th>\n",
       "    <th>33.600380</th>\n",
       "    <th>33.600380</th>\n",
       "    <th>1.027300</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>230</th>\n",
       "    <th>26.749916</th>\n",
       "    <th>38.045277</th>\n",
       "    <th>38.045277</th>\n",
       "    <th>1.171329</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>231</th>\n",
       "    <th>25.919899</th>\n",
       "    <th>32.549049</th>\n",
       "    <th>32.549049</th>\n",
       "    <th>1.005221</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>232</th>\n",
       "    <th>26.503141</th>\n",
       "    <th>34.799355</th>\n",
       "    <th>34.799355</th>\n",
       "    <th>1.126047</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>233</th>\n",
       "    <th>27.462088</th>\n",
       "    <th>34.370514</th>\n",
       "    <th>34.370514</th>\n",
       "    <th>1.119003</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>234</th>\n",
       "    <th>25.483414</th>\n",
       "    <th>32.796364</th>\n",
       "    <th>32.796364</th>\n",
       "    <th>0.986773</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>235</th>\n",
       "    <th>25.143938</th>\n",
       "    <th>33.543446</th>\n",
       "    <th>33.543446</th>\n",
       "    <th>0.921569</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>236</th>\n",
       "    <th>27.256798</th>\n",
       "    <th>41.890621</th>\n",
       "    <th>41.890621</th>\n",
       "    <th>1.472408</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>237</th>\n",
       "    <th>27.069658</th>\n",
       "    <th>34.268593</th>\n",
       "    <th>34.268593</th>\n",
       "    <th>1.095925</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>238</th>\n",
       "    <th>25.626146</th>\n",
       "    <th>32.390125</th>\n",
       "    <th>32.390125</th>\n",
       "    <th>0.965881</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>239</th>\n",
       "    <th>28.270185</th>\n",
       "    <th>35.112858</th>\n",
       "    <th>35.112858</th>\n",
       "    <th>1.197518</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>240</th>\n",
       "    <th>26.129293</th>\n",
       "    <th>31.299929</th>\n",
       "    <th>31.299929</th>\n",
       "    <th>0.950459</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>241</th>\n",
       "    <th>25.017155</th>\n",
       "    <th>33.250843</th>\n",
       "    <th>33.250843</th>\n",
       "    <th>0.981579</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>242</th>\n",
       "    <th>28.546240</th>\n",
       "    <th>38.895733</th>\n",
       "    <th>38.895733</th>\n",
       "    <th>1.299099</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>243</th>\n",
       "    <th>27.667696</th>\n",
       "    <th>40.412239</th>\n",
       "    <th>40.412239</th>\n",
       "    <th>1.208112</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>244</th>\n",
       "    <th>27.466463</th>\n",
       "    <th>34.926674</th>\n",
       "    <th>34.926674</th>\n",
       "    <th>0.994409</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>245</th>\n",
       "    <th>26.297838</th>\n",
       "    <th>35.675686</th>\n",
       "    <th>35.675686</th>\n",
       "    <th>1.041025</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>246</th>\n",
       "    <th>25.837664</th>\n",
       "    <th>31.276300</th>\n",
       "    <th>31.276300</th>\n",
       "    <th>0.877386</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>247</th>\n",
       "    <th>27.382563</th>\n",
       "    <th>34.964649</th>\n",
       "    <th>34.964649</th>\n",
       "    <th>0.996456</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>248</th>\n",
       "    <th>25.695036</th>\n",
       "    <th>31.864119</th>\n",
       "    <th>31.864119</th>\n",
       "    <th>0.902165</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>249</th>\n",
       "    <th>26.589243</th>\n",
       "    <th>34.604458</th>\n",
       "    <th>34.604458</th>\n",
       "    <th>1.085777</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>250</th>\n",
       "    <th>25.836847</th>\n",
       "    <th>32.285252</th>\n",
       "    <th>32.285252</th>\n",
       "    <th>0.935580</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>251</th>\n",
       "    <th>26.057444</th>\n",
       "    <th>32.139496</th>\n",
       "    <th>32.139496</th>\n",
       "    <th>0.998137</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>252</th>\n",
       "    <th>25.515123</th>\n",
       "    <th>33.318531</th>\n",
       "    <th>33.318531</th>\n",
       "    <th>0.952754</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>253</th>\n",
       "    <th>25.770452</th>\n",
       "    <th>35.468956</th>\n",
       "    <th>35.468956</th>\n",
       "    <th>1.093512</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>254</th>\n",
       "    <th>25.500908</th>\n",
       "    <th>35.852406</th>\n",
       "    <th>35.852406</th>\n",
       "    <th>1.162715</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>255</th>\n",
       "    <th>25.361259</th>\n",
       "    <th>32.820103</th>\n",
       "    <th>32.820103</th>\n",
       "    <th>0.955706</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>256</th>\n",
       "    <th>28.853706</th>\n",
       "    <th>35.856178</th>\n",
       "    <th>35.856178</th>\n",
       "    <th>1.043907</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>257</th>\n",
       "    <th>25.564240</th>\n",
       "    <th>36.450459</th>\n",
       "    <th>36.450459</th>\n",
       "    <th>0.984776</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>258</th>\n",
       "    <th>26.340834</th>\n",
       "    <th>32.367432</th>\n",
       "    <th>32.367432</th>\n",
       "    <th>0.936163</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>259</th>\n",
       "    <th>25.765715</th>\n",
       "    <th>32.469135</th>\n",
       "    <th>32.469135</th>\n",
       "    <th>1.002516</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>260</th>\n",
       "    <th>25.586151</th>\n",
       "    <th>32.392941</th>\n",
       "    <th>32.392941</th>\n",
       "    <th>0.996761</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>261</th>\n",
       "    <th>25.320780</th>\n",
       "    <th>32.391006</th>\n",
       "    <th>32.391006</th>\n",
       "    <th>0.960164</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>262</th>\n",
       "    <th>25.168524</th>\n",
       "    <th>31.501518</th>\n",
       "    <th>31.501518</th>\n",
       "    <th>0.886114</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>263</th>\n",
       "    <th>24.980967</th>\n",
       "    <th>37.570057</th>\n",
       "    <th>37.570057</th>\n",
       "    <th>0.978841</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>264</th>\n",
       "    <th>25.039856</th>\n",
       "    <th>33.572643</th>\n",
       "    <th>33.572643</th>\n",
       "    <th>1.001031</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>265</th>\n",
       "    <th>26.529257</th>\n",
       "    <th>33.933159</th>\n",
       "    <th>33.933159</th>\n",
       "    <th>0.951180</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>266</th>\n",
       "    <th>25.869755</th>\n",
       "    <th>32.125038</th>\n",
       "    <th>32.125038</th>\n",
       "    <th>0.968716</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>267</th>\n",
       "    <th>24.407232</th>\n",
       "    <th>31.170362</th>\n",
       "    <th>31.170362</th>\n",
       "    <th>0.911761</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>268</th>\n",
       "    <th>24.807287</th>\n",
       "    <th>34.830055</th>\n",
       "    <th>34.830055</th>\n",
       "    <th>1.118431</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>269</th>\n",
       "    <th>25.982519</th>\n",
       "    <th>34.059483</th>\n",
       "    <th>34.059483</th>\n",
       "    <th>1.065744</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>270</th>\n",
       "    <th>24.508297</th>\n",
       "    <th>32.183044</th>\n",
       "    <th>32.183044</th>\n",
       "    <th>0.935880</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>271</th>\n",
       "    <th>24.286854</th>\n",
       "    <th>31.299429</th>\n",
       "    <th>31.299429</th>\n",
       "    <th>0.905082</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>272</th>\n",
       "    <th>24.028584</th>\n",
       "    <th>32.051636</th>\n",
       "    <th>32.051636</th>\n",
       "    <th>0.900796</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>273</th>\n",
       "    <th>24.488087</th>\n",
       "    <th>32.366882</th>\n",
       "    <th>32.366882</th>\n",
       "    <th>0.886403</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>274</th>\n",
       "    <th>24.591457</th>\n",
       "    <th>36.249931</th>\n",
       "    <th>36.249931</th>\n",
       "    <th>1.071584</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>275</th>\n",
       "    <th>24.109695</th>\n",
       "    <th>31.641600</th>\n",
       "    <th>31.641600</th>\n",
       "    <th>0.910208</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>276</th>\n",
       "    <th>23.699841</th>\n",
       "    <th>32.668598</th>\n",
       "    <th>32.668598</th>\n",
       "    <th>0.984391</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>277</th>\n",
       "    <th>24.877148</th>\n",
       "    <th>38.544636</th>\n",
       "    <th>38.544636</th>\n",
       "    <th>1.053829</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>278</th>\n",
       "    <th>24.075441</th>\n",
       "    <th>32.226650</th>\n",
       "    <th>32.226650</th>\n",
       "    <th>0.917561</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>279</th>\n",
       "    <th>23.872923</th>\n",
       "    <th>33.202194</th>\n",
       "    <th>33.202194</th>\n",
       "    <th>0.996410</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>280</th>\n",
       "    <th>23.857298</th>\n",
       "    <th>33.910538</th>\n",
       "    <th>33.910538</th>\n",
       "    <th>1.005015</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>281</th>\n",
       "    <th>24.062830</th>\n",
       "    <th>35.335945</th>\n",
       "    <th>35.335945</th>\n",
       "    <th>1.182038</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>282</th>\n",
       "    <th>23.694611</th>\n",
       "    <th>33.267471</th>\n",
       "    <th>33.267471</th>\n",
       "    <th>1.059803</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>283</th>\n",
       "    <th>23.586744</th>\n",
       "    <th>31.372515</th>\n",
       "    <th>31.372515</th>\n",
       "    <th>1.002061</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>284</th>\n",
       "    <th>25.407211</th>\n",
       "    <th>32.189861</th>\n",
       "    <th>32.189861</th>\n",
       "    <th>1.021998</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>285</th>\n",
       "    <th>23.487856</th>\n",
       "    <th>32.176826</th>\n",
       "    <th>32.176826</th>\n",
       "    <th>0.873721</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>286</th>\n",
       "    <th>24.548525</th>\n",
       "    <th>33.411446</th>\n",
       "    <th>33.411446</th>\n",
       "    <th>1.070864</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>287</th>\n",
       "    <th>23.746799</th>\n",
       "    <th>31.334044</th>\n",
       "    <th>31.334044</th>\n",
       "    <th>0.924413</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>288</th>\n",
       "    <th>23.056448</th>\n",
       "    <th>30.744553</th>\n",
       "    <th>30.744553</th>\n",
       "    <th>0.848518</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>289</th>\n",
       "    <th>23.220255</th>\n",
       "    <th>29.609571</th>\n",
       "    <th>29.609571</th>\n",
       "    <th>0.902469</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>290</th>\n",
       "    <th>26.606550</th>\n",
       "    <th>31.455488</th>\n",
       "    <th>31.455488</th>\n",
       "    <th>0.951145</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>291</th>\n",
       "    <th>24.324184</th>\n",
       "    <th>31.123957</th>\n",
       "    <th>31.123957</th>\n",
       "    <th>0.884873</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>292</th>\n",
       "    <th>23.601318</th>\n",
       "    <th>30.639290</th>\n",
       "    <th>30.639290</th>\n",
       "    <th>0.908620</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>293</th>\n",
       "    <th>23.256067</th>\n",
       "    <th>30.693604</th>\n",
       "    <th>30.693604</th>\n",
       "    <th>0.951119</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>294</th>\n",
       "    <th>23.468546</th>\n",
       "    <th>36.644188</th>\n",
       "    <th>36.644188</th>\n",
       "    <th>0.935704</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>295</th>\n",
       "    <th>23.082041</th>\n",
       "    <th>35.079773</th>\n",
       "    <th>35.079773</th>\n",
       "    <th>0.991752</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>296</th>\n",
       "    <th>23.198311</th>\n",
       "    <th>31.531912</th>\n",
       "    <th>31.531912</th>\n",
       "    <th>0.893457</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>297</th>\n",
       "    <th>23.411459</th>\n",
       "    <th>32.130669</th>\n",
       "    <th>32.130669</th>\n",
       "    <th>0.916283</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>298</th>\n",
       "    <th>23.447931</th>\n",
       "    <th>32.620998</th>\n",
       "    <th>32.620998</th>\n",
       "    <th>0.837349</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>299</th>\n",
       "    <th>23.418173</th>\n",
       "    <th>36.132603</th>\n",
       "    <th>36.132603</th>\n",
       "    <th>1.105531</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>300</th>\n",
       "    <th>24.552629</th>\n",
       "    <th>32.620022</th>\n",
       "    <th>32.620022</th>\n",
       "    <th>0.930266</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>301</th>\n",
       "    <th>23.089695</th>\n",
       "    <th>32.890755</th>\n",
       "    <th>32.890755</th>\n",
       "    <th>0.905763</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>302</th>\n",
       "    <th>22.995024</th>\n",
       "    <th>31.730312</th>\n",
       "    <th>31.730312</th>\n",
       "    <th>0.911833</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>303</th>\n",
       "    <th>23.308723</th>\n",
       "    <th>37.217789</th>\n",
       "    <th>37.217789</th>\n",
       "    <th>0.971185</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>304</th>\n",
       "    <th>23.469707</th>\n",
       "    <th>31.198460</th>\n",
       "    <th>31.198460</th>\n",
       "    <th>0.882952</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>305</th>\n",
       "    <th>23.288631</th>\n",
       "    <th>29.943830</th>\n",
       "    <th>29.943830</th>\n",
       "    <th>0.869753</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>306</th>\n",
       "    <th>23.875689</th>\n",
       "    <th>32.404163</th>\n",
       "    <th>32.404163</th>\n",
       "    <th>1.034444</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>307</th>\n",
       "    <th>23.810047</th>\n",
       "    <th>33.021000</th>\n",
       "    <th>33.021000</th>\n",
       "    <th>0.936096</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>308</th>\n",
       "    <th>22.653074</th>\n",
       "    <th>30.908770</th>\n",
       "    <th>30.908770</th>\n",
       "    <th>0.882151</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>309</th>\n",
       "    <th>22.541355</th>\n",
       "    <th>29.819460</th>\n",
       "    <th>29.819460</th>\n",
       "    <th>0.885131</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>310</th>\n",
       "    <th>22.973125</th>\n",
       "    <th>31.631922</th>\n",
       "    <th>31.631922</th>\n",
       "    <th>0.969034</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>311</th>\n",
       "    <th>23.083841</th>\n",
       "    <th>30.547844</th>\n",
       "    <th>30.547844</th>\n",
       "    <th>0.883471</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>312</th>\n",
       "    <th>22.953493</th>\n",
       "    <th>30.353054</th>\n",
       "    <th>30.353054</th>\n",
       "    <th>0.878498</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>313</th>\n",
       "    <th>22.479532</th>\n",
       "    <th>31.809734</th>\n",
       "    <th>31.809734</th>\n",
       "    <th>0.896587</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>314</th>\n",
       "    <th>23.377104</th>\n",
       "    <th>33.697193</th>\n",
       "    <th>33.697193</th>\n",
       "    <th>0.922625</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>315</th>\n",
       "    <th>22.531416</th>\n",
       "    <th>29.855516</th>\n",
       "    <th>29.855516</th>\n",
       "    <th>0.803868</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>316</th>\n",
       "    <th>23.979380</th>\n",
       "    <th>36.531200</th>\n",
       "    <th>36.531200</th>\n",
       "    <th>1.105819</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>317</th>\n",
       "    <th>23.029348</th>\n",
       "    <th>33.710808</th>\n",
       "    <th>33.710808</th>\n",
       "    <th>1.006019</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>318</th>\n",
       "    <th>22.754444</th>\n",
       "    <th>31.665760</th>\n",
       "    <th>31.665760</th>\n",
       "    <th>0.914472</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>319</th>\n",
       "    <th>23.045223</th>\n",
       "    <th>31.995647</th>\n",
       "    <th>31.995647</th>\n",
       "    <th>0.987929</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>320</th>\n",
       "    <th>24.802938</th>\n",
       "    <th>36.426296</th>\n",
       "    <th>36.426296</th>\n",
       "    <th>1.242105</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>321</th>\n",
       "    <th>22.903860</th>\n",
       "    <th>31.389177</th>\n",
       "    <th>31.389177</th>\n",
       "    <th>0.922558</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>322</th>\n",
       "    <th>22.897532</th>\n",
       "    <th>30.757359</th>\n",
       "    <th>30.757359</th>\n",
       "    <th>0.898649</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>323</th>\n",
       "    <th>22.071262</th>\n",
       "    <th>31.176394</th>\n",
       "    <th>31.176394</th>\n",
       "    <th>0.926640</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>324</th>\n",
       "    <th>22.302603</th>\n",
       "    <th>33.496983</th>\n",
       "    <th>33.496983</th>\n",
       "    <th>1.003352</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>325</th>\n",
       "    <th>22.696529</th>\n",
       "    <th>33.157959</th>\n",
       "    <th>33.157959</th>\n",
       "    <th>1.019879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>326</th>\n",
       "    <th>22.206831</th>\n",
       "    <th>31.187950</th>\n",
       "    <th>31.187950</th>\n",
       "    <th>0.910020</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>327</th>\n",
       "    <th>22.430916</th>\n",
       "    <th>32.108555</th>\n",
       "    <th>32.108555</th>\n",
       "    <th>0.836346</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>328</th>\n",
       "    <th>23.363632</th>\n",
       "    <th>30.739073</th>\n",
       "    <th>30.739073</th>\n",
       "    <th>0.895830</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>329</th>\n",
       "    <th>21.920441</th>\n",
       "    <th>30.016001</th>\n",
       "    <th>30.016001</th>\n",
       "    <th>0.869675</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>330</th>\n",
       "    <th>22.654621</th>\n",
       "    <th>31.877682</th>\n",
       "    <th>31.877682</th>\n",
       "    <th>0.965833</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>331</th>\n",
       "    <th>22.456778</th>\n",
       "    <th>30.879557</th>\n",
       "    <th>30.879557</th>\n",
       "    <th>0.890036</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>332</th>\n",
       "    <th>21.835112</th>\n",
       "    <th>30.547707</th>\n",
       "    <th>30.547707</th>\n",
       "    <th>0.892623</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>333</th>\n",
       "    <th>22.950558</th>\n",
       "    <th>32.231445</th>\n",
       "    <th>32.231445</th>\n",
       "    <th>0.961082</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>334</th>\n",
       "    <th>22.641918</th>\n",
       "    <th>31.190937</th>\n",
       "    <th>31.190937</th>\n",
       "    <th>0.883059</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>335</th>\n",
       "    <th>22.391863</th>\n",
       "    <th>30.077229</th>\n",
       "    <th>30.077229</th>\n",
       "    <th>0.887276</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>336</th>\n",
       "    <th>21.825678</th>\n",
       "    <th>31.201479</th>\n",
       "    <th>31.201479</th>\n",
       "    <th>0.874746</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>337</th>\n",
       "    <th>21.929396</th>\n",
       "    <th>31.703747</th>\n",
       "    <th>31.703747</th>\n",
       "    <th>0.913503</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>338</th>\n",
       "    <th>22.114321</th>\n",
       "    <th>31.262888</th>\n",
       "    <th>31.262888</th>\n",
       "    <th>0.897873</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>339</th>\n",
       "    <th>22.255375</th>\n",
       "    <th>32.257145</th>\n",
       "    <th>32.257145</th>\n",
       "    <th>0.987418</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>340</th>\n",
       "    <th>21.516308</th>\n",
       "    <th>31.055069</th>\n",
       "    <th>31.055069</th>\n",
       "    <th>0.866648</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>341</th>\n",
       "    <th>21.780066</th>\n",
       "    <th>31.119778</th>\n",
       "    <th>31.119778</th>\n",
       "    <th>0.877982</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>342</th>\n",
       "    <th>21.928745</th>\n",
       "    <th>30.912075</th>\n",
       "    <th>30.912075</th>\n",
       "    <th>0.899803</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>343</th>\n",
       "    <th>23.069935</th>\n",
       "    <th>30.149940</th>\n",
       "    <th>30.149940</th>\n",
       "    <th>0.861979</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>344</th>\n",
       "    <th>21.972891</th>\n",
       "    <th>31.098553</th>\n",
       "    <th>31.098553</th>\n",
       "    <th>0.907958</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>345</th>\n",
       "    <th>22.763105</th>\n",
       "    <th>30.626051</th>\n",
       "    <th>30.626051</th>\n",
       "    <th>0.877580</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>346</th>\n",
       "    <th>21.794235</th>\n",
       "    <th>29.366579</th>\n",
       "    <th>29.366579</th>\n",
       "    <th>0.857488</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>347</th>\n",
       "    <th>22.008078</th>\n",
       "    <th>31.416065</th>\n",
       "    <th>31.416065</th>\n",
       "    <th>0.885416</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>348</th>\n",
       "    <th>21.296263</th>\n",
       "    <th>37.282524</th>\n",
       "    <th>37.282524</th>\n",
       "    <th>0.900297</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>349</th>\n",
       "    <th>21.912718</th>\n",
       "    <th>31.073544</th>\n",
       "    <th>31.073544</th>\n",
       "    <th>0.883205</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>350</th>\n",
       "    <th>21.693811</th>\n",
       "    <th>32.051483</th>\n",
       "    <th>32.051483</th>\n",
       "    <th>0.909462</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>351</th>\n",
       "    <th>21.124010</th>\n",
       "    <th>31.240728</th>\n",
       "    <th>31.240728</th>\n",
       "    <th>0.854305</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>352</th>\n",
       "    <th>21.423153</th>\n",
       "    <th>32.100945</th>\n",
       "    <th>32.100945</th>\n",
       "    <th>0.848509</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>353</th>\n",
       "    <th>21.261118</th>\n",
       "    <th>29.452200</th>\n",
       "    <th>29.452200</th>\n",
       "    <th>0.858050</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>354</th>\n",
       "    <th>21.611090</th>\n",
       "    <th>30.465622</th>\n",
       "    <th>30.465622</th>\n",
       "    <th>0.801553</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>355</th>\n",
       "    <th>21.001186</th>\n",
       "    <th>30.024084</th>\n",
       "    <th>30.024084</th>\n",
       "    <th>0.824635</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>356</th>\n",
       "    <th>22.011847</th>\n",
       "    <th>30.820822</th>\n",
       "    <th>30.820822</th>\n",
       "    <th>0.851494</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>357</th>\n",
       "    <th>21.753456</th>\n",
       "    <th>30.837734</th>\n",
       "    <th>30.837734</th>\n",
       "    <th>0.868175</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>358</th>\n",
       "    <th>21.731434</th>\n",
       "    <th>34.118027</th>\n",
       "    <th>34.118027</th>\n",
       "    <th>1.015847</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>359</th>\n",
       "    <th>21.168015</th>\n",
       "    <th>30.982712</th>\n",
       "    <th>30.982712</th>\n",
       "    <th>0.840182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>360</th>\n",
       "    <th>22.695019</th>\n",
       "    <th>33.550556</th>\n",
       "    <th>33.550556</th>\n",
       "    <th>1.028217</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>361</th>\n",
       "    <th>21.332977</th>\n",
       "    <th>32.812496</th>\n",
       "    <th>32.812496</th>\n",
       "    <th>0.821068</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>362</th>\n",
       "    <th>21.554502</th>\n",
       "    <th>29.820454</th>\n",
       "    <th>29.820454</th>\n",
       "    <th>0.838471</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>363</th>\n",
       "    <th>21.550817</th>\n",
       "    <th>29.094250</th>\n",
       "    <th>29.094250</th>\n",
       "    <th>0.819488</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>364</th>\n",
       "    <th>21.270784</th>\n",
       "    <th>30.155769</th>\n",
       "    <th>30.155769</th>\n",
       "    <th>0.833044</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>365</th>\n",
       "    <th>21.196297</th>\n",
       "    <th>30.056921</th>\n",
       "    <th>30.056921</th>\n",
       "    <th>0.864647</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>366</th>\n",
       "    <th>20.805273</th>\n",
       "    <th>32.347954</th>\n",
       "    <th>32.347954</th>\n",
       "    <th>0.806086</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>367</th>\n",
       "    <th>20.950691</th>\n",
       "    <th>29.996269</th>\n",
       "    <th>29.996269</th>\n",
       "    <th>0.875347</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>368</th>\n",
       "    <th>20.785179</th>\n",
       "    <th>30.727106</th>\n",
       "    <th>30.727106</th>\n",
       "    <th>0.810430</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>369</th>\n",
       "    <th>20.711361</th>\n",
       "    <th>30.829437</th>\n",
       "    <th>30.829437</th>\n",
       "    <th>0.856253</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>370</th>\n",
       "    <th>22.384754</th>\n",
       "    <th>32.001652</th>\n",
       "    <th>32.001652</th>\n",
       "    <th>0.851036</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>371</th>\n",
       "    <th>21.168989</th>\n",
       "    <th>32.221874</th>\n",
       "    <th>32.221874</th>\n",
       "    <th>0.842884</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>372</th>\n",
       "    <th>20.893677</th>\n",
       "    <th>29.698332</th>\n",
       "    <th>29.698332</th>\n",
       "    <th>0.857130</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>373</th>\n",
       "    <th>21.091854</th>\n",
       "    <th>30.021406</th>\n",
       "    <th>30.021406</th>\n",
       "    <th>0.825766</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>374</th>\n",
       "    <th>20.901320</th>\n",
       "    <th>30.207632</th>\n",
       "    <th>30.207632</th>\n",
       "    <th>0.851027</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>375</th>\n",
       "    <th>20.465696</th>\n",
       "    <th>29.873453</th>\n",
       "    <th>29.873453</th>\n",
       "    <th>0.868507</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>376</th>\n",
       "    <th>20.910698</th>\n",
       "    <th>32.481735</th>\n",
       "    <th>32.481735</th>\n",
       "    <th>0.818452</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>377</th>\n",
       "    <th>22.071100</th>\n",
       "    <th>37.372929</th>\n",
       "    <th>37.372929</th>\n",
       "    <th>1.280528</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>378</th>\n",
       "    <th>21.523090</th>\n",
       "    <th>30.677456</th>\n",
       "    <th>30.677456</th>\n",
       "    <th>0.894106</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>379</th>\n",
       "    <th>21.051147</th>\n",
       "    <th>30.056681</th>\n",
       "    <th>30.056681</th>\n",
       "    <th>0.844973</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>380</th>\n",
       "    <th>21.277016</th>\n",
       "    <th>37.887123</th>\n",
       "    <th>37.887123</th>\n",
       "    <th>0.899381</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>381</th>\n",
       "    <th>21.058306</th>\n",
       "    <th>29.201265</th>\n",
       "    <th>29.201265</th>\n",
       "    <th>0.873207</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>382</th>\n",
       "    <th>20.397820</th>\n",
       "    <th>35.085377</th>\n",
       "    <th>35.085377</th>\n",
       "    <th>0.849859</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>383</th>\n",
       "    <th>20.428373</th>\n",
       "    <th>30.272606</th>\n",
       "    <th>30.272606</th>\n",
       "    <th>0.825856</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>384</th>\n",
       "    <th>20.639648</th>\n",
       "    <th>30.199144</th>\n",
       "    <th>30.199144</th>\n",
       "    <th>0.808246</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>385</th>\n",
       "    <th>20.853455</th>\n",
       "    <th>30.764318</th>\n",
       "    <th>30.764318</th>\n",
       "    <th>0.831441</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>386</th>\n",
       "    <th>20.715252</th>\n",
       "    <th>32.586666</th>\n",
       "    <th>32.586666</th>\n",
       "    <th>0.932385</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>387</th>\n",
       "    <th>22.734861</th>\n",
       "    <th>32.516033</th>\n",
       "    <th>32.516033</th>\n",
       "    <th>0.914500</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>388</th>\n",
       "    <th>21.534632</th>\n",
       "    <th>29.612213</th>\n",
       "    <th>29.612213</th>\n",
       "    <th>0.789968</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>389</th>\n",
       "    <th>20.910292</th>\n",
       "    <th>29.556524</th>\n",
       "    <th>29.556524</th>\n",
       "    <th>0.877321</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>390</th>\n",
       "    <th>20.401121</th>\n",
       "    <th>29.579050</th>\n",
       "    <th>29.579050</th>\n",
       "    <th>0.867446</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>391</th>\n",
       "    <th>21.685516</th>\n",
       "    <th>29.570728</th>\n",
       "    <th>29.570728</th>\n",
       "    <th>0.843794</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>392</th>\n",
       "    <th>21.184284</th>\n",
       "    <th>31.258287</th>\n",
       "    <th>31.258287</th>\n",
       "    <th>0.880590</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>393</th>\n",
       "    <th>20.740654</th>\n",
       "    <th>31.646885</th>\n",
       "    <th>31.646885</th>\n",
       "    <th>0.847608</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>394</th>\n",
       "    <th>20.377882</th>\n",
       "    <th>28.979078</th>\n",
       "    <th>28.979078</th>\n",
       "    <th>0.834601</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>395</th>\n",
       "    <th>20.823029</th>\n",
       "    <th>29.595682</th>\n",
       "    <th>29.595682</th>\n",
       "    <th>0.823866</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>396</th>\n",
       "    <th>21.010220</th>\n",
       "    <th>32.761490</th>\n",
       "    <th>32.761490</th>\n",
       "    <th>0.906316</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>397</th>\n",
       "    <th>21.691399</th>\n",
       "    <th>29.872484</th>\n",
       "    <th>29.872484</th>\n",
       "    <th>0.846540</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>398</th>\n",
       "    <th>20.535309</th>\n",
       "    <th>28.862680</th>\n",
       "    <th>28.862680</th>\n",
       "    <th>0.815005</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>399</th>\n",
       "    <th>21.257097</th>\n",
       "    <th>30.425934</th>\n",
       "    <th>30.425934</th>\n",
       "    <th>0.904062</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>400</th>\n",
       "    <th>20.441315</th>\n",
       "    <th>29.542583</th>\n",
       "    <th>29.542583</th>\n",
       "    <th>0.824441</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>401</th>\n",
       "    <th>21.436716</th>\n",
       "    <th>29.673115</th>\n",
       "    <th>29.673115</th>\n",
       "    <th>0.846149</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>402</th>\n",
       "    <th>20.377682</th>\n",
       "    <th>29.961506</th>\n",
       "    <th>29.961506</th>\n",
       "    <th>0.889306</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>403</th>\n",
       "    <th>20.296585</th>\n",
       "    <th>34.585514</th>\n",
       "    <th>34.585514</th>\n",
       "    <th>0.788908</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>404</th>\n",
       "    <th>20.323652</th>\n",
       "    <th>28.946640</th>\n",
       "    <th>28.946640</th>\n",
       "    <th>0.820772</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>405</th>\n",
       "    <th>20.474659</th>\n",
       "    <th>29.274656</th>\n",
       "    <th>29.274656</th>\n",
       "    <th>0.828503</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>406</th>\n",
       "    <th>20.870571</th>\n",
       "    <th>28.745275</th>\n",
       "    <th>28.745275</th>\n",
       "    <th>0.787625</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>407</th>\n",
       "    <th>20.306068</th>\n",
       "    <th>29.546560</th>\n",
       "    <th>29.546560</th>\n",
       "    <th>0.834301</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>408</th>\n",
       "    <th>20.925884</th>\n",
       "    <th>30.670446</th>\n",
       "    <th>30.670446</th>\n",
       "    <th>0.883481</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>409</th>\n",
       "    <th>20.282574</th>\n",
       "    <th>30.109241</th>\n",
       "    <th>30.109241</th>\n",
       "    <th>0.793878</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>410</th>\n",
       "    <th>20.248425</th>\n",
       "    <th>31.711435</th>\n",
       "    <th>31.711435</th>\n",
       "    <th>0.812172</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>411</th>\n",
       "    <th>20.402033</th>\n",
       "    <th>31.665981</th>\n",
       "    <th>31.665981</th>\n",
       "    <th>0.854510</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>412</th>\n",
       "    <th>20.905537</th>\n",
       "    <th>30.501328</th>\n",
       "    <th>30.501328</th>\n",
       "    <th>0.838574</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>413</th>\n",
       "    <th>20.176268</th>\n",
       "    <th>29.862406</th>\n",
       "    <th>29.862406</th>\n",
       "    <th>0.832434</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>414</th>\n",
       "    <th>20.176748</th>\n",
       "    <th>31.632710</th>\n",
       "    <th>31.632710</th>\n",
       "    <th>0.808291</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>415</th>\n",
       "    <th>20.250065</th>\n",
       "    <th>32.482403</th>\n",
       "    <th>32.482403</th>\n",
       "    <th>0.792285</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>416</th>\n",
       "    <th>20.054466</th>\n",
       "    <th>29.077406</th>\n",
       "    <th>29.077406</th>\n",
       "    <th>0.782281</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>417</th>\n",
       "    <th>22.013273</th>\n",
       "    <th>31.963310</th>\n",
       "    <th>31.963310</th>\n",
       "    <th>0.938765</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>418</th>\n",
       "    <th>20.554232</th>\n",
       "    <th>28.941990</th>\n",
       "    <th>28.941990</th>\n",
       "    <th>0.823217</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>419</th>\n",
       "    <th>20.034082</th>\n",
       "    <th>29.493748</th>\n",
       "    <th>29.493748</th>\n",
       "    <th>0.839953</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>420</th>\n",
       "    <th>19.805950</th>\n",
       "    <th>28.680843</th>\n",
       "    <th>28.680843</th>\n",
       "    <th>0.793167</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>421</th>\n",
       "    <th>20.012754</th>\n",
       "    <th>28.625881</th>\n",
       "    <th>28.625881</th>\n",
       "    <th>0.820954</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>422</th>\n",
       "    <th>20.571121</th>\n",
       "    <th>29.856009</th>\n",
       "    <th>29.856009</th>\n",
       "    <th>0.830377</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>423</th>\n",
       "    <th>20.893597</th>\n",
       "    <th>29.019703</th>\n",
       "    <th>29.019703</th>\n",
       "    <th>0.818913</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>424</th>\n",
       "    <th>20.022091</th>\n",
       "    <th>29.251379</th>\n",
       "    <th>29.251379</th>\n",
       "    <th>0.821455</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>425</th>\n",
       "    <th>19.953663</th>\n",
       "    <th>29.986902</th>\n",
       "    <th>29.986902</th>\n",
       "    <th>0.847029</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>426</th>\n",
       "    <th>20.175449</th>\n",
       "    <th>32.254326</th>\n",
       "    <th>32.254326</th>\n",
       "    <th>0.826874</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>427</th>\n",
       "    <th>20.121197</th>\n",
       "    <th>28.251232</th>\n",
       "    <th>28.251232</th>\n",
       "    <th>0.765793</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>428</th>\n",
       "    <th>19.703075</th>\n",
       "    <th>34.320843</th>\n",
       "    <th>34.320843</th>\n",
       "    <th>0.848079</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>429</th>\n",
       "    <th>19.625452</th>\n",
       "    <th>29.194220</th>\n",
       "    <th>29.194220</th>\n",
       "    <th>0.827396</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>430</th>\n",
       "    <th>19.823961</th>\n",
       "    <th>29.234501</th>\n",
       "    <th>29.234501</th>\n",
       "    <th>0.866847</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>431</th>\n",
       "    <th>20.077082</th>\n",
       "    <th>31.221746</th>\n",
       "    <th>31.221746</th>\n",
       "    <th>0.785974</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>432</th>\n",
       "    <th>19.562960</th>\n",
       "    <th>28.876188</th>\n",
       "    <th>28.876188</th>\n",
       "    <th>0.798593</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>433</th>\n",
       "    <th>20.124300</th>\n",
       "    <th>30.601238</th>\n",
       "    <th>30.601238</th>\n",
       "    <th>0.816879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>434</th>\n",
       "    <th>20.246696</th>\n",
       "    <th>28.178406</th>\n",
       "    <th>28.178406</th>\n",
       "    <th>0.838261</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>435</th>\n",
       "    <th>20.770285</th>\n",
       "    <th>28.125269</th>\n",
       "    <th>28.125269</th>\n",
       "    <th>0.795397</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>436</th>\n",
       "    <th>19.879068</th>\n",
       "    <th>29.259546</th>\n",
       "    <th>29.259546</th>\n",
       "    <th>0.823099</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>437</th>\n",
       "    <th>19.691528</th>\n",
       "    <th>28.939587</th>\n",
       "    <th>28.939587</th>\n",
       "    <th>0.808469</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>438</th>\n",
       "    <th>19.599457</th>\n",
       "    <th>28.823338</th>\n",
       "    <th>28.823338</th>\n",
       "    <th>0.827701</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>439</th>\n",
       "    <th>19.700653</th>\n",
       "    <th>28.093182</th>\n",
       "    <th>28.093182</th>\n",
       "    <th>0.808369</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>440</th>\n",
       "    <th>19.761353</th>\n",
       "    <th>29.189981</th>\n",
       "    <th>29.189981</th>\n",
       "    <th>0.833378</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>441</th>\n",
       "    <th>20.525223</th>\n",
       "    <th>30.768887</th>\n",
       "    <th>30.768887</th>\n",
       "    <th>0.937565</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>442</th>\n",
       "    <th>19.884026</th>\n",
       "    <th>28.702646</th>\n",
       "    <th>28.702646</th>\n",
       "    <th>0.790882</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>443</th>\n",
       "    <th>19.504898</th>\n",
       "    <th>28.761332</th>\n",
       "    <th>28.761332</th>\n",
       "    <th>0.773987</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>444</th>\n",
       "    <th>19.643684</th>\n",
       "    <th>28.014353</th>\n",
       "    <th>28.014353</th>\n",
       "    <th>0.813970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>445</th>\n",
       "    <th>19.844217</th>\n",
       "    <th>30.297432</th>\n",
       "    <th>30.297432</th>\n",
       "    <th>0.845744</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>446</th>\n",
       "    <th>19.496357</th>\n",
       "    <th>29.172981</th>\n",
       "    <th>29.172981</th>\n",
       "    <th>0.781155</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>447</th>\n",
       "    <th>19.644041</th>\n",
       "    <th>28.487400</th>\n",
       "    <th>28.487400</th>\n",
       "    <th>0.807753</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>448</th>\n",
       "    <th>19.365408</th>\n",
       "    <th>30.819744</th>\n",
       "    <th>30.819744</th>\n",
       "    <th>0.843257</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>449</th>\n",
       "    <th>19.521078</th>\n",
       "    <th>31.532700</th>\n",
       "    <th>31.532700</th>\n",
       "    <th>0.841266</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>450</th>\n",
       "    <th>19.456493</th>\n",
       "    <th>30.356873</th>\n",
       "    <th>30.356873</th>\n",
       "    <th>0.785845</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>451</th>\n",
       "    <th>19.648565</th>\n",
       "    <th>31.745150</th>\n",
       "    <th>31.745150</th>\n",
       "    <th>0.812724</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>452</th>\n",
       "    <th>19.593699</th>\n",
       "    <th>30.675659</th>\n",
       "    <th>30.675659</th>\n",
       "    <th>0.812390</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>453</th>\n",
       "    <th>20.048771</th>\n",
       "    <th>28.119640</th>\n",
       "    <th>28.119640</th>\n",
       "    <th>0.805636</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>454</th>\n",
       "    <th>19.289371</th>\n",
       "    <th>29.465569</th>\n",
       "    <th>29.465569</th>\n",
       "    <th>0.858308</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>455</th>\n",
       "    <th>19.906805</th>\n",
       "    <th>35.977478</th>\n",
       "    <th>35.977478</th>\n",
       "    <th>0.801470</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>456</th>\n",
       "    <th>19.246086</th>\n",
       "    <th>32.566921</th>\n",
       "    <th>32.566921</th>\n",
       "    <th>0.785453</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>457</th>\n",
       "    <th>19.046930</th>\n",
       "    <th>33.387695</th>\n",
       "    <th>33.387695</th>\n",
       "    <th>0.777202</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>458</th>\n",
       "    <th>20.706711</th>\n",
       "    <th>27.973434</th>\n",
       "    <th>27.973434</th>\n",
       "    <th>0.771132</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>459</th>\n",
       "    <th>19.532320</th>\n",
       "    <th>28.505552</th>\n",
       "    <th>28.505552</th>\n",
       "    <th>0.833862</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>460</th>\n",
       "    <th>19.190317</th>\n",
       "    <th>28.970850</th>\n",
       "    <th>28.970850</th>\n",
       "    <th>0.809045</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>461</th>\n",
       "    <th>19.118349</th>\n",
       "    <th>30.058697</th>\n",
       "    <th>30.058697</th>\n",
       "    <th>0.821459</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>462</th>\n",
       "    <th>18.995913</th>\n",
       "    <th>33.846058</th>\n",
       "    <th>33.846058</th>\n",
       "    <th>0.811710</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>463</th>\n",
       "    <th>19.434963</th>\n",
       "    <th>29.147669</th>\n",
       "    <th>29.147669</th>\n",
       "    <th>0.811450</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>464</th>\n",
       "    <th>19.144217</th>\n",
       "    <th>30.309462</th>\n",
       "    <th>30.309462</th>\n",
       "    <th>0.829537</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>465</th>\n",
       "    <th>19.286913</th>\n",
       "    <th>28.443413</th>\n",
       "    <th>28.443413</th>\n",
       "    <th>0.830206</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>466</th>\n",
       "    <th>19.138081</th>\n",
       "    <th>29.790941</th>\n",
       "    <th>29.790941</th>\n",
       "    <th>0.803123</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>467</th>\n",
       "    <th>19.566339</th>\n",
       "    <th>29.688787</th>\n",
       "    <th>29.688787</th>\n",
       "    <th>0.831225</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>468</th>\n",
       "    <th>19.017393</th>\n",
       "    <th>30.432978</th>\n",
       "    <th>30.432978</th>\n",
       "    <th>0.800006</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>469</th>\n",
       "    <th>19.200897</th>\n",
       "    <th>28.826660</th>\n",
       "    <th>28.826660</th>\n",
       "    <th>0.806616</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>470</th>\n",
       "    <th>19.077814</th>\n",
       "    <th>29.637991</th>\n",
       "    <th>29.637991</th>\n",
       "    <th>0.872812</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>471</th>\n",
       "    <th>19.297106</th>\n",
       "    <th>28.089090</th>\n",
       "    <th>28.089090</th>\n",
       "    <th>0.797123</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>472</th>\n",
       "    <th>19.248178</th>\n",
       "    <th>28.323915</th>\n",
       "    <th>28.323915</th>\n",
       "    <th>0.797561</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>473</th>\n",
       "    <th>19.471537</th>\n",
       "    <th>33.361996</th>\n",
       "    <th>33.361996</th>\n",
       "    <th>0.825895</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>474</th>\n",
       "    <th>19.137493</th>\n",
       "    <th>28.747469</th>\n",
       "    <th>28.747469</th>\n",
       "    <th>0.839534</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>475</th>\n",
       "    <th>19.087740</th>\n",
       "    <th>29.672346</th>\n",
       "    <th>29.672346</th>\n",
       "    <th>0.823931</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>476</th>\n",
       "    <th>19.093016</th>\n",
       "    <th>28.717791</th>\n",
       "    <th>28.717791</th>\n",
       "    <th>0.797999</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>477</th>\n",
       "    <th>19.288471</th>\n",
       "    <th>30.481037</th>\n",
       "    <th>30.481037</th>\n",
       "    <th>0.837468</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>478</th>\n",
       "    <th>18.715424</th>\n",
       "    <th>29.380821</th>\n",
       "    <th>29.380821</th>\n",
       "    <th>0.817564</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>479</th>\n",
       "    <th>18.850544</th>\n",
       "    <th>27.921947</th>\n",
       "    <th>27.921947</th>\n",
       "    <th>0.836809</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>480</th>\n",
       "    <th>19.967514</th>\n",
       "    <th>28.410894</th>\n",
       "    <th>28.410894</th>\n",
       "    <th>0.787467</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>481</th>\n",
       "    <th>19.000586</th>\n",
       "    <th>28.329756</th>\n",
       "    <th>28.329756</th>\n",
       "    <th>0.791516</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>482</th>\n",
       "    <th>19.012230</th>\n",
       "    <th>29.234493</th>\n",
       "    <th>29.234493</th>\n",
       "    <th>0.831665</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>483</th>\n",
       "    <th>19.427523</th>\n",
       "    <th>30.080803</th>\n",
       "    <th>30.080803</th>\n",
       "    <th>0.831058</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>484</th>\n",
       "    <th>19.091721</th>\n",
       "    <th>28.247032</th>\n",
       "    <th>28.247032</th>\n",
       "    <th>0.788731</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>485</th>\n",
       "    <th>19.320009</th>\n",
       "    <th>29.452084</th>\n",
       "    <th>29.452084</th>\n",
       "    <th>0.802656</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>486</th>\n",
       "    <th>18.684084</th>\n",
       "    <th>28.529751</th>\n",
       "    <th>28.529751</th>\n",
       "    <th>0.829016</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>487</th>\n",
       "    <th>19.066122</th>\n",
       "    <th>28.370623</th>\n",
       "    <th>28.370623</th>\n",
       "    <th>0.825954</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>488</th>\n",
       "    <th>18.752859</th>\n",
       "    <th>27.702585</th>\n",
       "    <th>27.702585</th>\n",
       "    <th>0.810707</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>489</th>\n",
       "    <th>18.915010</th>\n",
       "    <th>28.855024</th>\n",
       "    <th>28.855024</th>\n",
       "    <th>0.833701</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>490</th>\n",
       "    <th>18.890432</th>\n",
       "    <th>28.233650</th>\n",
       "    <th>28.233650</th>\n",
       "    <th>0.793416</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>491</th>\n",
       "    <th>18.661421</th>\n",
       "    <th>28.412495</th>\n",
       "    <th>28.412495</th>\n",
       "    <th>0.807095</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>492</th>\n",
       "    <th>18.625416</th>\n",
       "    <th>32.525017</th>\n",
       "    <th>32.525017</th>\n",
       "    <th>0.813924</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>493</th>\n",
       "    <th>19.042862</th>\n",
       "    <th>30.305803</th>\n",
       "    <th>30.305803</th>\n",
       "    <th>0.822418</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>494</th>\n",
       "    <th>18.830856</th>\n",
       "    <th>29.509148</th>\n",
       "    <th>29.509148</th>\n",
       "    <th>0.847423</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>495</th>\n",
       "    <th>18.604158</th>\n",
       "    <th>32.330986</th>\n",
       "    <th>32.330986</th>\n",
       "    <th>0.800880</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>496</th>\n",
       "    <th>18.548861</th>\n",
       "    <th>36.686813</th>\n",
       "    <th>36.686813</th>\n",
       "    <th>0.822160</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>497</th>\n",
       "    <th>18.734352</th>\n",
       "    <th>31.414082</th>\n",
       "    <th>31.414082</th>\n",
       "    <th>0.879719</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>498</th>\n",
       "    <th>21.489693</th>\n",
       "    <th>33.244453</th>\n",
       "    <th>33.244453</th>\n",
       "    <th>0.979087</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>499</th>\n",
       "    <th>19.575710</th>\n",
       "    <th>28.878981</th>\n",
       "    <th>28.878981</th>\n",
       "    <th>0.818416</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>500</th>\n",
       "    <th>19.235252</th>\n",
       "    <th>35.798065</th>\n",
       "    <th>35.798065</th>\n",
       "    <th>0.843767</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>501</th>\n",
       "    <th>18.970251</th>\n",
       "    <th>31.063808</th>\n",
       "    <th>31.063808</th>\n",
       "    <th>0.834666</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>502</th>\n",
       "    <th>18.794472</th>\n",
       "    <th>30.663954</th>\n",
       "    <th>30.663954</th>\n",
       "    <th>0.808094</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>503</th>\n",
       "    <th>19.522507</th>\n",
       "    <th>28.788944</th>\n",
       "    <th>28.788944</th>\n",
       "    <th>0.808638</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>504</th>\n",
       "    <th>19.042694</th>\n",
       "    <th>31.979513</th>\n",
       "    <th>31.979513</th>\n",
       "    <th>0.797237</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>505</th>\n",
       "    <th>18.731030</th>\n",
       "    <th>29.441072</th>\n",
       "    <th>29.441072</th>\n",
       "    <th>0.796457</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>506</th>\n",
       "    <th>18.685034</th>\n",
       "    <th>32.853844</th>\n",
       "    <th>32.853844</th>\n",
       "    <th>0.812230</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>507</th>\n",
       "    <th>18.687244</th>\n",
       "    <th>27.935768</th>\n",
       "    <th>27.935768</th>\n",
       "    <th>0.802739</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>508</th>\n",
       "    <th>19.053106</th>\n",
       "    <th>36.384274</th>\n",
       "    <th>36.384274</th>\n",
       "    <th>0.833893</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>509</th>\n",
       "    <th>18.487600</th>\n",
       "    <th>28.993994</th>\n",
       "    <th>28.993994</th>\n",
       "    <th>0.767522</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>510</th>\n",
       "    <th>18.930454</th>\n",
       "    <th>29.036371</th>\n",
       "    <th>29.036371</th>\n",
       "    <th>0.815594</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>511</th>\n",
       "    <th>18.757000</th>\n",
       "    <th>28.855068</th>\n",
       "    <th>28.855068</th>\n",
       "    <th>0.805587</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>512</th>\n",
       "    <th>19.914585</th>\n",
       "    <th>29.817194</th>\n",
       "    <th>29.817194</th>\n",
       "    <th>0.816499</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>513</th>\n",
       "    <th>19.164135</th>\n",
       "    <th>29.108395</th>\n",
       "    <th>29.108395</th>\n",
       "    <th>0.786850</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>514</th>\n",
       "    <th>18.590864</th>\n",
       "    <th>28.818531</th>\n",
       "    <th>28.818531</th>\n",
       "    <th>0.817846</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>515</th>\n",
       "    <th>18.586807</th>\n",
       "    <th>27.661900</th>\n",
       "    <th>27.661900</th>\n",
       "    <th>0.791212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>516</th>\n",
       "    <th>18.600199</th>\n",
       "    <th>31.925699</th>\n",
       "    <th>31.925699</th>\n",
       "    <th>0.803102</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>517</th>\n",
       "    <th>19.260864</th>\n",
       "    <th>29.666262</th>\n",
       "    <th>29.666262</th>\n",
       "    <th>0.806355</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>518</th>\n",
       "    <th>18.585262</th>\n",
       "    <th>28.622156</th>\n",
       "    <th>28.622156</th>\n",
       "    <th>0.813290</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>519</th>\n",
       "    <th>18.432613</th>\n",
       "    <th>28.319435</th>\n",
       "    <th>28.319435</th>\n",
       "    <th>0.770556</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>520</th>\n",
       "    <th>18.740717</th>\n",
       "    <th>32.556770</th>\n",
       "    <th>32.556770</th>\n",
       "    <th>0.781645</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>521</th>\n",
       "    <th>18.342276</th>\n",
       "    <th>28.982544</th>\n",
       "    <th>28.982544</th>\n",
       "    <th>0.807039</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>522</th>\n",
       "    <th>18.419756</th>\n",
       "    <th>29.330782</th>\n",
       "    <th>29.330782</th>\n",
       "    <th>0.828422</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>523</th>\n",
       "    <th>18.596813</th>\n",
       "    <th>27.356737</th>\n",
       "    <th>27.356737</th>\n",
       "    <th>0.777246</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>524</th>\n",
       "    <th>18.479000</th>\n",
       "    <th>28.914984</th>\n",
       "    <th>28.914984</th>\n",
       "    <th>0.789031</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>525</th>\n",
       "    <th>18.286955</th>\n",
       "    <th>28.859993</th>\n",
       "    <th>28.859993</th>\n",
       "    <th>0.817922</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>526</th>\n",
       "    <th>18.300722</th>\n",
       "    <th>28.679691</th>\n",
       "    <th>28.679691</th>\n",
       "    <th>0.801912</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>527</th>\n",
       "    <th>18.411259</th>\n",
       "    <th>28.591101</th>\n",
       "    <th>28.591101</th>\n",
       "    <th>0.790872</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>528</th>\n",
       "    <th>18.466677</th>\n",
       "    <th>31.310768</th>\n",
       "    <th>31.310768</th>\n",
       "    <th>0.780634</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>529</th>\n",
       "    <th>18.748308</th>\n",
       "    <th>28.666344</th>\n",
       "    <th>28.666344</th>\n",
       "    <th>0.808915</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>530</th>\n",
       "    <th>18.389208</th>\n",
       "    <th>29.133528</th>\n",
       "    <th>29.133528</th>\n",
       "    <th>0.802566</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>531</th>\n",
       "    <th>19.157234</th>\n",
       "    <th>32.655762</th>\n",
       "    <th>32.655762</th>\n",
       "    <th>0.883345</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>532</th>\n",
       "    <th>18.492657</th>\n",
       "    <th>33.494709</th>\n",
       "    <th>33.494709</th>\n",
       "    <th>0.815507</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>533</th>\n",
       "    <th>18.171228</th>\n",
       "    <th>27.968496</th>\n",
       "    <th>27.968496</th>\n",
       "    <th>0.808676</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>534</th>\n",
       "    <th>18.365082</th>\n",
       "    <th>27.503294</th>\n",
       "    <th>27.503294</th>\n",
       "    <th>0.786708</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>535</th>\n",
       "    <th>18.288067</th>\n",
       "    <th>29.489275</th>\n",
       "    <th>29.489275</th>\n",
       "    <th>0.782427</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>536</th>\n",
       "    <th>18.122732</th>\n",
       "    <th>30.631668</th>\n",
       "    <th>30.631668</th>\n",
       "    <th>0.796378</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>537</th>\n",
       "    <th>18.323353</th>\n",
       "    <th>27.308079</th>\n",
       "    <th>27.308079</th>\n",
       "    <th>0.790524</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>538</th>\n",
       "    <th>18.275864</th>\n",
       "    <th>30.051615</th>\n",
       "    <th>30.051615</th>\n",
       "    <th>0.808777</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>539</th>\n",
       "    <th>18.293583</th>\n",
       "    <th>29.219412</th>\n",
       "    <th>29.219412</th>\n",
       "    <th>0.806456</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>540</th>\n",
       "    <th>17.954775</th>\n",
       "    <th>28.541504</th>\n",
       "    <th>28.541504</th>\n",
       "    <th>0.805398</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>541</th>\n",
       "    <th>18.066191</th>\n",
       "    <th>28.261681</th>\n",
       "    <th>28.261681</th>\n",
       "    <th>0.818398</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>542</th>\n",
       "    <th>17.964024</th>\n",
       "    <th>28.225599</th>\n",
       "    <th>28.225599</th>\n",
       "    <th>0.800760</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>543</th>\n",
       "    <th>18.867937</th>\n",
       "    <th>28.684799</th>\n",
       "    <th>28.684799</th>\n",
       "    <th>0.796958</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>544</th>\n",
       "    <th>18.307156</th>\n",
       "    <th>27.848352</th>\n",
       "    <th>27.848352</th>\n",
       "    <th>0.815688</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>545</th>\n",
       "    <th>18.222130</th>\n",
       "    <th>27.622999</th>\n",
       "    <th>27.622999</th>\n",
       "    <th>0.802000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>546</th>\n",
       "    <th>18.121170</th>\n",
       "    <th>28.313740</th>\n",
       "    <th>28.313740</th>\n",
       "    <th>0.784680</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>547</th>\n",
       "    <th>18.081829</th>\n",
       "    <th>28.714735</th>\n",
       "    <th>28.714735</th>\n",
       "    <th>0.809561</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>548</th>\n",
       "    <th>17.929947</th>\n",
       "    <th>28.144703</th>\n",
       "    <th>28.144703</th>\n",
       "    <th>0.822806</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>549</th>\n",
       "    <th>18.456024</th>\n",
       "    <th>27.154905</th>\n",
       "    <th>27.154905</th>\n",
       "    <th>0.800913</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>550</th>\n",
       "    <th>18.283314</th>\n",
       "    <th>27.209984</th>\n",
       "    <th>27.209984</th>\n",
       "    <th>0.758437</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>551</th>\n",
       "    <th>18.178699</th>\n",
       "    <th>28.201706</th>\n",
       "    <th>28.201706</th>\n",
       "    <th>0.812562</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>552</th>\n",
       "    <th>17.990282</th>\n",
       "    <th>31.273769</th>\n",
       "    <th>31.273769</th>\n",
       "    <th>0.792225</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>553</th>\n",
       "    <th>18.173210</th>\n",
       "    <th>32.221287</th>\n",
       "    <th>32.221287</th>\n",
       "    <th>0.809525</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>554</th>\n",
       "    <th>18.046230</th>\n",
       "    <th>32.659435</th>\n",
       "    <th>32.659435</th>\n",
       "    <th>0.783259</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>555</th>\n",
       "    <th>18.059402</th>\n",
       "    <th>28.472418</th>\n",
       "    <th>28.472418</th>\n",
       "    <th>0.824534</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>556</th>\n",
       "    <th>17.947958</th>\n",
       "    <th>29.319368</th>\n",
       "    <th>29.319368</th>\n",
       "    <th>0.807815</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>557</th>\n",
       "    <th>17.903797</th>\n",
       "    <th>26.825281</th>\n",
       "    <th>26.825281</th>\n",
       "    <th>0.795264</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>558</th>\n",
       "    <th>17.928417</th>\n",
       "    <th>28.792412</th>\n",
       "    <th>28.792412</th>\n",
       "    <th>0.791423</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>559</th>\n",
       "    <th>17.954567</th>\n",
       "    <th>32.185600</th>\n",
       "    <th>32.185600</th>\n",
       "    <th>0.773542</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>560</th>\n",
       "    <th>18.110603</th>\n",
       "    <th>27.884933</th>\n",
       "    <th>27.884933</th>\n",
       "    <th>0.781463</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>561</th>\n",
       "    <th>18.145023</th>\n",
       "    <th>28.899963</th>\n",
       "    <th>28.899963</th>\n",
       "    <th>0.804412</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>562</th>\n",
       "    <th>17.746674</th>\n",
       "    <th>28.720743</th>\n",
       "    <th>28.720743</th>\n",
       "    <th>0.795243</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>563</th>\n",
       "    <th>17.885849</th>\n",
       "    <th>29.133881</th>\n",
       "    <th>29.133881</th>\n",
       "    <th>0.780703</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>564</th>\n",
       "    <th>18.646643</th>\n",
       "    <th>28.584585</th>\n",
       "    <th>28.584585</th>\n",
       "    <th>0.848611</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>565</th>\n",
       "    <th>17.976086</th>\n",
       "    <th>28.227648</th>\n",
       "    <th>28.227648</th>\n",
       "    <th>0.802864</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>566</th>\n",
       "    <th>17.998764</th>\n",
       "    <th>29.081551</th>\n",
       "    <th>29.081551</th>\n",
       "    <th>0.853913</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>567</th>\n",
       "    <th>17.917789</th>\n",
       "    <th>28.360426</th>\n",
       "    <th>28.360426</th>\n",
       "    <th>0.797072</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>568</th>\n",
       "    <th>17.933283</th>\n",
       "    <th>28.649822</th>\n",
       "    <th>28.649822</th>\n",
       "    <th>0.802096</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>569</th>\n",
       "    <th>21.522024</th>\n",
       "    <th>28.711487</th>\n",
       "    <th>28.711487</th>\n",
       "    <th>0.809169</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>570</th>\n",
       "    <th>18.529943</th>\n",
       "    <th>28.712231</th>\n",
       "    <th>28.712231</th>\n",
       "    <th>0.785515</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>571</th>\n",
       "    <th>18.288063</th>\n",
       "    <th>28.371950</th>\n",
       "    <th>28.371950</th>\n",
       "    <th>0.785830</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>572</th>\n",
       "    <th>17.972334</th>\n",
       "    <th>27.679632</th>\n",
       "    <th>27.679632</th>\n",
       "    <th>0.784952</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>573</th>\n",
       "    <th>18.002291</th>\n",
       "    <th>28.303097</th>\n",
       "    <th>28.303097</th>\n",
       "    <th>0.797607</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>574</th>\n",
       "    <th>17.853533</th>\n",
       "    <th>29.422052</th>\n",
       "    <th>29.422052</th>\n",
       "    <th>0.785309</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>575</th>\n",
       "    <th>17.687721</th>\n",
       "    <th>28.551638</th>\n",
       "    <th>28.551638</th>\n",
       "    <th>0.798720</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>576</th>\n",
       "    <th>17.769146</th>\n",
       "    <th>28.783237</th>\n",
       "    <th>28.783237</th>\n",
       "    <th>0.810928</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>577</th>\n",
       "    <th>18.325815</th>\n",
       "    <th>28.381691</th>\n",
       "    <th>28.381691</th>\n",
       "    <th>0.786729</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>578</th>\n",
       "    <th>17.722073</th>\n",
       "    <th>28.524647</th>\n",
       "    <th>28.524647</th>\n",
       "    <th>0.810884</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>579</th>\n",
       "    <th>18.020409</th>\n",
       "    <th>28.306974</th>\n",
       "    <th>28.306974</th>\n",
       "    <th>0.842434</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>580</th>\n",
       "    <th>17.638700</th>\n",
       "    <th>27.007015</th>\n",
       "    <th>27.007015</th>\n",
       "    <th>0.804997</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>581</th>\n",
       "    <th>17.689589</th>\n",
       "    <th>28.884638</th>\n",
       "    <th>28.884638</th>\n",
       "    <th>0.803661</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>582</th>\n",
       "    <th>17.843819</th>\n",
       "    <th>28.789013</th>\n",
       "    <th>28.789013</th>\n",
       "    <th>0.838733</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>583</th>\n",
       "    <th>17.941050</th>\n",
       "    <th>28.134237</th>\n",
       "    <th>28.134237</th>\n",
       "    <th>0.822000</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>584</th>\n",
       "    <th>17.932430</th>\n",
       "    <th>27.928556</th>\n",
       "    <th>27.928556</th>\n",
       "    <th>0.816374</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>585</th>\n",
       "    <th>17.798983</th>\n",
       "    <th>28.115591</th>\n",
       "    <th>28.115591</th>\n",
       "    <th>0.831435</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>586</th>\n",
       "    <th>17.846436</th>\n",
       "    <th>27.533155</th>\n",
       "    <th>27.533155</th>\n",
       "    <th>0.780688</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>587</th>\n",
       "    <th>17.880156</th>\n",
       "    <th>27.417297</th>\n",
       "    <th>27.417297</th>\n",
       "    <th>0.794735</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>588</th>\n",
       "    <th>17.729916</th>\n",
       "    <th>34.643021</th>\n",
       "    <th>34.643021</th>\n",
       "    <th>0.806141</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>589</th>\n",
       "    <th>17.771526</th>\n",
       "    <th>30.294962</th>\n",
       "    <th>30.294962</th>\n",
       "    <th>0.804006</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>590</th>\n",
       "    <th>17.615005</th>\n",
       "    <th>28.008430</th>\n",
       "    <th>28.008430</th>\n",
       "    <th>0.810962</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>591</th>\n",
       "    <th>17.708654</th>\n",
       "    <th>29.333738</th>\n",
       "    <th>29.333738</th>\n",
       "    <th>0.784271</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>592</th>\n",
       "    <th>18.061981</th>\n",
       "    <th>28.397350</th>\n",
       "    <th>28.397350</th>\n",
       "    <th>0.794360</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>593</th>\n",
       "    <th>17.578438</th>\n",
       "    <th>27.662868</th>\n",
       "    <th>27.662868</th>\n",
       "    <th>0.789975</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>594</th>\n",
       "    <th>17.602802</th>\n",
       "    <th>27.934034</th>\n",
       "    <th>27.934034</th>\n",
       "    <th>0.798738</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>595</th>\n",
       "    <th>17.891249</th>\n",
       "    <th>28.692678</th>\n",
       "    <th>28.692678</th>\n",
       "    <th>0.831615</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>596</th>\n",
       "    <th>17.748182</th>\n",
       "    <th>31.623512</th>\n",
       "    <th>31.623512</th>\n",
       "    <th>0.794870</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>597</th>\n",
       "    <th>17.845011</th>\n",
       "    <th>27.498922</th>\n",
       "    <th>27.498922</th>\n",
       "    <th>0.787116</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>598</th>\n",
       "    <th>17.625898</th>\n",
       "    <th>29.436575</th>\n",
       "    <th>29.436575</th>\n",
       "    <th>0.803320</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>599</th>\n",
       "    <th>17.421249</th>\n",
       "    <th>27.799156</th>\n",
       "    <th>27.799156</th>\n",
       "    <th>0.785211</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>600</th>\n",
       "    <th>17.897877</th>\n",
       "    <th>27.987938</th>\n",
       "    <th>27.987938</th>\n",
       "    <th>0.803181</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>601</th>\n",
       "    <th>17.703045</th>\n",
       "    <th>28.317219</th>\n",
       "    <th>28.317219</th>\n",
       "    <th>0.795314</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>602</th>\n",
       "    <th>17.639812</th>\n",
       "    <th>26.977180</th>\n",
       "    <th>26.977180</th>\n",
       "    <th>0.811252</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>603</th>\n",
       "    <th>17.499931</th>\n",
       "    <th>28.375774</th>\n",
       "    <th>28.375774</th>\n",
       "    <th>0.831028</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>604</th>\n",
       "    <th>17.442181</th>\n",
       "    <th>28.418228</th>\n",
       "    <th>28.418228</th>\n",
       "    <th>0.786331</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>605</th>\n",
       "    <th>17.405857</th>\n",
       "    <th>27.312347</th>\n",
       "    <th>27.312347</th>\n",
       "    <th>0.804111</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>606</th>\n",
       "    <th>17.251495</th>\n",
       "    <th>27.428782</th>\n",
       "    <th>27.428782</th>\n",
       "    <th>0.796183</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>607</th>\n",
       "    <th>17.557735</th>\n",
       "    <th>28.910631</th>\n",
       "    <th>28.910631</th>\n",
       "    <th>0.806243</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>608</th>\n",
       "    <th>17.665724</th>\n",
       "    <th>28.874775</th>\n",
       "    <th>28.874775</th>\n",
       "    <th>0.793076</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>609</th>\n",
       "    <th>17.481850</th>\n",
       "    <th>27.788662</th>\n",
       "    <th>27.788662</th>\n",
       "    <th>0.788307</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>610</th>\n",
       "    <th>17.323893</th>\n",
       "    <th>28.850548</th>\n",
       "    <th>28.850548</th>\n",
       "    <th>0.786959</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>611</th>\n",
       "    <th>17.502729</th>\n",
       "    <th>27.875359</th>\n",
       "    <th>27.875359</th>\n",
       "    <th>0.804534</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>612</th>\n",
       "    <th>17.468634</th>\n",
       "    <th>27.730978</th>\n",
       "    <th>27.730978</th>\n",
       "    <th>0.770689</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>613</th>\n",
       "    <th>17.442465</th>\n",
       "    <th>27.976254</th>\n",
       "    <th>27.976254</th>\n",
       "    <th>0.823252</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>614</th>\n",
       "    <th>17.354700</th>\n",
       "    <th>27.969843</th>\n",
       "    <th>27.969843</th>\n",
       "    <th>0.807511</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>615</th>\n",
       "    <th>17.623312</th>\n",
       "    <th>28.360256</th>\n",
       "    <th>28.360256</th>\n",
       "    <th>0.788857</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>616</th>\n",
       "    <th>17.452040</th>\n",
       "    <th>29.479416</th>\n",
       "    <th>29.479416</th>\n",
       "    <th>0.787155</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>617</th>\n",
       "    <th>17.280842</th>\n",
       "    <th>27.197399</th>\n",
       "    <th>27.197399</th>\n",
       "    <th>0.774638</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>618</th>\n",
       "    <th>17.251572</th>\n",
       "    <th>27.242329</th>\n",
       "    <th>27.242329</th>\n",
       "    <th>0.793468</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>619</th>\n",
       "    <th>17.392990</th>\n",
       "    <th>28.543219</th>\n",
       "    <th>28.543219</th>\n",
       "    <th>0.780622</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>620</th>\n",
       "    <th>17.320471</th>\n",
       "    <th>27.375168</th>\n",
       "    <th>27.375168</th>\n",
       "    <th>0.786253</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>621</th>\n",
       "    <th>17.310198</th>\n",
       "    <th>28.276903</th>\n",
       "    <th>28.276903</th>\n",
       "    <th>0.798509</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>622</th>\n",
       "    <th>17.273369</th>\n",
       "    <th>27.490688</th>\n",
       "    <th>27.490688</th>\n",
       "    <th>0.800756</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>623</th>\n",
       "    <th>17.506470</th>\n",
       "    <th>28.576750</th>\n",
       "    <th>28.576750</th>\n",
       "    <th>0.816155</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>624</th>\n",
       "    <th>17.392796</th>\n",
       "    <th>28.728769</th>\n",
       "    <th>28.728769</th>\n",
       "    <th>0.798268</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>625</th>\n",
       "    <th>17.370989</th>\n",
       "    <th>27.022993</th>\n",
       "    <th>27.022993</th>\n",
       "    <th>0.770885</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>626</th>\n",
       "    <th>17.119162</th>\n",
       "    <th>27.550957</th>\n",
       "    <th>27.550957</th>\n",
       "    <th>0.788462</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>627</th>\n",
       "    <th>17.578735</th>\n",
       "    <th>29.108131</th>\n",
       "    <th>29.108131</th>\n",
       "    <th>0.790754</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>628</th>\n",
       "    <th>17.668715</th>\n",
       "    <th>27.485071</th>\n",
       "    <th>27.485071</th>\n",
       "    <th>0.780120</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>629</th>\n",
       "    <th>17.414627</th>\n",
       "    <th>28.285006</th>\n",
       "    <th>28.285006</th>\n",
       "    <th>0.830884</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>630</th>\n",
       "    <th>17.117641</th>\n",
       "    <th>29.621269</th>\n",
       "    <th>29.621269</th>\n",
       "    <th>0.793023</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>631</th>\n",
       "    <th>17.352013</th>\n",
       "    <th>27.058331</th>\n",
       "    <th>27.058331</th>\n",
       "    <th>0.778065</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>632</th>\n",
       "    <th>17.312277</th>\n",
       "    <th>27.926588</th>\n",
       "    <th>27.926588</th>\n",
       "    <th>0.799945</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>633</th>\n",
       "    <th>17.380318</th>\n",
       "    <th>28.067144</th>\n",
       "    <th>28.067144</th>\n",
       "    <th>0.815895</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>634</th>\n",
       "    <th>17.207094</th>\n",
       "    <th>28.071600</th>\n",
       "    <th>28.071600</th>\n",
       "    <th>0.787623</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>635</th>\n",
       "    <th>17.205881</th>\n",
       "    <th>28.937841</th>\n",
       "    <th>28.937841</th>\n",
       "    <th>0.789494</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>636</th>\n",
       "    <th>17.091520</th>\n",
       "    <th>27.108519</th>\n",
       "    <th>27.108519</th>\n",
       "    <th>0.788637</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>637</th>\n",
       "    <th>17.167465</th>\n",
       "    <th>28.971544</th>\n",
       "    <th>28.971544</th>\n",
       "    <th>0.795279</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>638</th>\n",
       "    <th>17.259142</th>\n",
       "    <th>27.664297</th>\n",
       "    <th>27.664297</th>\n",
       "    <th>0.810648</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>639</th>\n",
       "    <th>17.216381</th>\n",
       "    <th>27.810003</th>\n",
       "    <th>27.810003</th>\n",
       "    <th>0.806325</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>640</th>\n",
       "    <th>17.099447</th>\n",
       "    <th>27.365841</th>\n",
       "    <th>27.365841</th>\n",
       "    <th>0.784213</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>641</th>\n",
       "    <th>17.303518</th>\n",
       "    <th>28.339977</th>\n",
       "    <th>28.339977</th>\n",
       "    <th>0.794872</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>642</th>\n",
       "    <th>17.181255</th>\n",
       "    <th>27.198538</th>\n",
       "    <th>27.198538</th>\n",
       "    <th>0.811263</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>643</th>\n",
       "    <th>17.106701</th>\n",
       "    <th>26.510437</th>\n",
       "    <th>26.510437</th>\n",
       "    <th>0.787544</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>644</th>\n",
       "    <th>17.247372</th>\n",
       "    <th>27.242809</th>\n",
       "    <th>27.242809</th>\n",
       "    <th>0.769073</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>645</th>\n",
       "    <th>17.074533</th>\n",
       "    <th>28.296816</th>\n",
       "    <th>28.296816</th>\n",
       "    <th>0.802879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>646</th>\n",
       "    <th>16.989622</th>\n",
       "    <th>28.599751</th>\n",
       "    <th>28.599751</th>\n",
       "    <th>0.789129</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>647</th>\n",
       "    <th>17.119640</th>\n",
       "    <th>26.839716</th>\n",
       "    <th>26.839716</th>\n",
       "    <th>0.798851</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>648</th>\n",
       "    <th>16.892845</th>\n",
       "    <th>26.786354</th>\n",
       "    <th>26.786354</th>\n",
       "    <th>0.781286</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>649</th>\n",
       "    <th>17.102020</th>\n",
       "    <th>26.898312</th>\n",
       "    <th>26.898312</th>\n",
       "    <th>0.781657</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>650</th>\n",
       "    <th>17.213791</th>\n",
       "    <th>27.534040</th>\n",
       "    <th>27.534040</th>\n",
       "    <th>0.795574</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>651</th>\n",
       "    <th>16.957556</th>\n",
       "    <th>27.271097</th>\n",
       "    <th>27.271097</th>\n",
       "    <th>0.766666</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>652</th>\n",
       "    <th>16.928381</th>\n",
       "    <th>28.039469</th>\n",
       "    <th>28.039469</th>\n",
       "    <th>0.809415</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>653</th>\n",
       "    <th>17.033014</th>\n",
       "    <th>27.025023</th>\n",
       "    <th>27.025023</th>\n",
       "    <th>0.804320</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>654</th>\n",
       "    <th>16.966272</th>\n",
       "    <th>27.154984</th>\n",
       "    <th>27.154984</th>\n",
       "    <th>0.792331</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>655</th>\n",
       "    <th>17.198063</th>\n",
       "    <th>29.588659</th>\n",
       "    <th>29.588659</th>\n",
       "    <th>0.799991</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>656</th>\n",
       "    <th>16.940140</th>\n",
       "    <th>27.388393</th>\n",
       "    <th>27.388393</th>\n",
       "    <th>0.788060</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>657</th>\n",
       "    <th>16.902292</th>\n",
       "    <th>27.177265</th>\n",
       "    <th>27.177265</th>\n",
       "    <th>0.789067</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>658</th>\n",
       "    <th>17.071827</th>\n",
       "    <th>27.059528</th>\n",
       "    <th>27.059528</th>\n",
       "    <th>0.781199</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>659</th>\n",
       "    <th>16.922876</th>\n",
       "    <th>27.313709</th>\n",
       "    <th>27.313709</th>\n",
       "    <th>0.770448</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>660</th>\n",
       "    <th>17.121283</th>\n",
       "    <th>28.013788</th>\n",
       "    <th>28.013788</th>\n",
       "    <th>0.797554</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>661</th>\n",
       "    <th>16.867857</th>\n",
       "    <th>27.943600</th>\n",
       "    <th>27.943600</th>\n",
       "    <th>0.777280</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>662</th>\n",
       "    <th>17.007591</th>\n",
       "    <th>26.410524</th>\n",
       "    <th>26.410524</th>\n",
       "    <th>0.772415</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>663</th>\n",
       "    <th>17.538517</th>\n",
       "    <th>27.290934</th>\n",
       "    <th>27.290934</th>\n",
       "    <th>0.792031</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>664</th>\n",
       "    <th>17.104950</th>\n",
       "    <th>27.240818</th>\n",
       "    <th>27.240818</th>\n",
       "    <th>0.787426</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>665</th>\n",
       "    <th>16.871302</th>\n",
       "    <th>27.132812</th>\n",
       "    <th>27.132812</th>\n",
       "    <th>0.773973</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>666</th>\n",
       "    <th>16.873585</th>\n",
       "    <th>28.067532</th>\n",
       "    <th>28.067532</th>\n",
       "    <th>0.807919</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>667</th>\n",
       "    <th>16.958727</th>\n",
       "    <th>27.339703</th>\n",
       "    <th>27.339703</th>\n",
       "    <th>0.771055</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>668</th>\n",
       "    <th>16.707430</th>\n",
       "    <th>28.278860</th>\n",
       "    <th>28.278860</th>\n",
       "    <th>0.792168</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>669</th>\n",
       "    <th>16.808928</th>\n",
       "    <th>26.903929</th>\n",
       "    <th>26.903929</th>\n",
       "    <th>0.784007</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>670</th>\n",
       "    <th>16.765100</th>\n",
       "    <th>27.281694</th>\n",
       "    <th>27.281694</th>\n",
       "    <th>0.759059</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>671</th>\n",
       "    <th>16.709122</th>\n",
       "    <th>29.886410</th>\n",
       "    <th>29.886410</th>\n",
       "    <th>0.789253</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>672</th>\n",
       "    <th>16.777184</th>\n",
       "    <th>28.782948</th>\n",
       "    <th>28.782948</th>\n",
       "    <th>0.789873</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>673</th>\n",
       "    <th>16.821814</th>\n",
       "    <th>27.015800</th>\n",
       "    <th>27.015800</th>\n",
       "    <th>0.771316</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>674</th>\n",
       "    <th>16.806858</th>\n",
       "    <th>28.796600</th>\n",
       "    <th>28.796600</th>\n",
       "    <th>0.777490</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>675</th>\n",
       "    <th>17.074207</th>\n",
       "    <th>27.535559</th>\n",
       "    <th>27.535559</th>\n",
       "    <th>0.785934</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>676</th>\n",
       "    <th>16.805508</th>\n",
       "    <th>26.888769</th>\n",
       "    <th>26.888769</th>\n",
       "    <th>0.778445</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>677</th>\n",
       "    <th>16.683338</th>\n",
       "    <th>27.455185</th>\n",
       "    <th>27.455185</th>\n",
       "    <th>0.802354</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>678</th>\n",
       "    <th>16.705442</th>\n",
       "    <th>26.830231</th>\n",
       "    <th>26.830231</th>\n",
       "    <th>0.776792</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>679</th>\n",
       "    <th>16.684431</th>\n",
       "    <th>27.452147</th>\n",
       "    <th>27.452147</th>\n",
       "    <th>0.796341</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>680</th>\n",
       "    <th>16.841024</th>\n",
       "    <th>26.478893</th>\n",
       "    <th>26.478893</th>\n",
       "    <th>0.756678</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>681</th>\n",
       "    <th>16.688116</th>\n",
       "    <th>27.349028</th>\n",
       "    <th>27.349028</th>\n",
       "    <th>0.816917</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>682</th>\n",
       "    <th>16.736755</th>\n",
       "    <th>28.431538</th>\n",
       "    <th>28.431538</th>\n",
       "    <th>0.780882</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>683</th>\n",
       "    <th>16.802292</th>\n",
       "    <th>29.574768</th>\n",
       "    <th>29.574768</th>\n",
       "    <th>0.794963</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>684</th>\n",
       "    <th>16.644146</th>\n",
       "    <th>29.714413</th>\n",
       "    <th>29.714413</th>\n",
       "    <th>0.769809</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>685</th>\n",
       "    <th>16.744667</th>\n",
       "    <th>27.234247</th>\n",
       "    <th>27.234247</th>\n",
       "    <th>0.770231</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>686</th>\n",
       "    <th>16.625433</th>\n",
       "    <th>26.388720</th>\n",
       "    <th>26.388720</th>\n",
       "    <th>0.775414</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>687</th>\n",
       "    <th>16.800825</th>\n",
       "    <th>27.142574</th>\n",
       "    <th>27.142574</th>\n",
       "    <th>0.785395</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>688</th>\n",
       "    <th>16.713234</th>\n",
       "    <th>27.370226</th>\n",
       "    <th>27.370226</th>\n",
       "    <th>0.787948</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>689</th>\n",
       "    <th>16.763170</th>\n",
       "    <th>26.557604</th>\n",
       "    <th>26.557604</th>\n",
       "    <th>0.770133</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>690</th>\n",
       "    <th>16.667793</th>\n",
       "    <th>27.292660</th>\n",
       "    <th>27.292660</th>\n",
       "    <th>0.799290</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>691</th>\n",
       "    <th>16.511072</th>\n",
       "    <th>25.987612</th>\n",
       "    <th>25.987612</th>\n",
       "    <th>0.784037</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>692</th>\n",
       "    <th>16.767830</th>\n",
       "    <th>26.823372</th>\n",
       "    <th>26.823372</th>\n",
       "    <th>0.781418</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>693</th>\n",
       "    <th>16.572884</th>\n",
       "    <th>26.562025</th>\n",
       "    <th>26.562025</th>\n",
       "    <th>0.786220</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>694</th>\n",
       "    <th>16.544050</th>\n",
       "    <th>26.013071</th>\n",
       "    <th>26.013071</th>\n",
       "    <th>0.785235</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>695</th>\n",
       "    <th>16.539724</th>\n",
       "    <th>27.241650</th>\n",
       "    <th>27.241650</th>\n",
       "    <th>0.796031</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>696</th>\n",
       "    <th>16.660828</th>\n",
       "    <th>26.886257</th>\n",
       "    <th>26.886257</th>\n",
       "    <th>0.777752</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>697</th>\n",
       "    <th>16.520720</th>\n",
       "    <th>27.407625</th>\n",
       "    <th>27.407625</th>\n",
       "    <th>0.792651</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>698</th>\n",
       "    <th>16.654369</th>\n",
       "    <th>27.682993</th>\n",
       "    <th>27.682993</th>\n",
       "    <th>0.795769</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>699</th>\n",
       "    <th>16.646852</th>\n",
       "    <th>25.970144</th>\n",
       "    <th>25.970144</th>\n",
       "    <th>0.782772</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>700</th>\n",
       "    <th>16.499769</th>\n",
       "    <th>26.431351</th>\n",
       "    <th>26.431351</th>\n",
       "    <th>0.769097</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>701</th>\n",
       "    <th>16.709023</th>\n",
       "    <th>26.461344</th>\n",
       "    <th>26.461344</th>\n",
       "    <th>0.781995</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>702</th>\n",
       "    <th>16.522114</th>\n",
       "    <th>26.948515</th>\n",
       "    <th>26.948515</th>\n",
       "    <th>0.814278</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>703</th>\n",
       "    <th>16.486153</th>\n",
       "    <th>26.706081</th>\n",
       "    <th>26.706081</th>\n",
       "    <th>0.776816</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>704</th>\n",
       "    <th>16.389822</th>\n",
       "    <th>27.398598</th>\n",
       "    <th>27.398598</th>\n",
       "    <th>0.803426</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>705</th>\n",
       "    <th>16.530134</th>\n",
       "    <th>26.748653</th>\n",
       "    <th>26.748653</th>\n",
       "    <th>0.784518</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>706</th>\n",
       "    <th>16.700533</th>\n",
       "    <th>27.837446</th>\n",
       "    <th>27.837446</th>\n",
       "    <th>0.780310</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>707</th>\n",
       "    <th>16.512947</th>\n",
       "    <th>26.447731</th>\n",
       "    <th>26.447731</th>\n",
       "    <th>0.771325</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>708</th>\n",
       "    <th>16.352173</th>\n",
       "    <th>27.166069</th>\n",
       "    <th>27.166069</th>\n",
       "    <th>0.769641</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>709</th>\n",
       "    <th>16.551634</th>\n",
       "    <th>26.927567</th>\n",
       "    <th>26.927567</th>\n",
       "    <th>0.777702</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>710</th>\n",
       "    <th>16.560049</th>\n",
       "    <th>26.494499</th>\n",
       "    <th>26.494499</th>\n",
       "    <th>0.778497</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>711</th>\n",
       "    <th>16.439978</th>\n",
       "    <th>26.590538</th>\n",
       "    <th>26.590538</th>\n",
       "    <th>0.777355</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>712</th>\n",
       "    <th>16.515909</th>\n",
       "    <th>26.718426</th>\n",
       "    <th>26.718426</th>\n",
       "    <th>0.788142</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>713</th>\n",
       "    <th>16.495802</th>\n",
       "    <th>27.504875</th>\n",
       "    <th>27.504875</th>\n",
       "    <th>0.784242</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>714</th>\n",
       "    <th>16.462254</th>\n",
       "    <th>26.597376</th>\n",
       "    <th>26.597376</th>\n",
       "    <th>0.759546</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>715</th>\n",
       "    <th>16.362782</th>\n",
       "    <th>26.566847</th>\n",
       "    <th>26.566847</th>\n",
       "    <th>0.779002</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>716</th>\n",
       "    <th>16.520308</th>\n",
       "    <th>27.104631</th>\n",
       "    <th>27.104631</th>\n",
       "    <th>0.787567</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>717</th>\n",
       "    <th>16.421644</th>\n",
       "    <th>28.063381</th>\n",
       "    <th>28.063381</th>\n",
       "    <th>0.775310</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>718</th>\n",
       "    <th>16.392958</th>\n",
       "    <th>27.587543</th>\n",
       "    <th>27.587543</th>\n",
       "    <th>0.781921</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>719</th>\n",
       "    <th>16.958408</th>\n",
       "    <th>27.343338</th>\n",
       "    <th>27.343338</th>\n",
       "    <th>0.770135</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>720</th>\n",
       "    <th>16.520132</th>\n",
       "    <th>26.215372</th>\n",
       "    <th>26.215372</th>\n",
       "    <th>0.771877</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>721</th>\n",
       "    <th>16.328648</th>\n",
       "    <th>27.000862</th>\n",
       "    <th>27.000862</th>\n",
       "    <th>0.775650</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>722</th>\n",
       "    <th>16.351032</th>\n",
       "    <th>27.189129</th>\n",
       "    <th>27.189129</th>\n",
       "    <th>0.772957</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>723</th>\n",
       "    <th>16.393864</th>\n",
       "    <th>27.017946</th>\n",
       "    <th>27.017946</th>\n",
       "    <th>0.783211</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>724</th>\n",
       "    <th>16.540356</th>\n",
       "    <th>27.877306</th>\n",
       "    <th>27.877306</th>\n",
       "    <th>0.793030</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>725</th>\n",
       "    <th>16.354229</th>\n",
       "    <th>27.904057</th>\n",
       "    <th>27.904057</th>\n",
       "    <th>0.802253</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>726</th>\n",
       "    <th>16.299385</th>\n",
       "    <th>26.749178</th>\n",
       "    <th>26.749178</th>\n",
       "    <th>0.772832</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>727</th>\n",
       "    <th>16.357649</th>\n",
       "    <th>27.533400</th>\n",
       "    <th>27.533400</th>\n",
       "    <th>0.785337</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>728</th>\n",
       "    <th>16.388748</th>\n",
       "    <th>27.145300</th>\n",
       "    <th>27.145300</th>\n",
       "    <th>0.771143</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>729</th>\n",
       "    <th>16.134628</th>\n",
       "    <th>27.220512</th>\n",
       "    <th>27.220512</th>\n",
       "    <th>0.776294</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>730</th>\n",
       "    <th>16.253176</th>\n",
       "    <th>26.701609</th>\n",
       "    <th>26.701609</th>\n",
       "    <th>0.778763</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>731</th>\n",
       "    <th>16.388430</th>\n",
       "    <th>27.196880</th>\n",
       "    <th>27.196880</th>\n",
       "    <th>0.782008</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>732</th>\n",
       "    <th>16.295607</th>\n",
       "    <th>26.320356</th>\n",
       "    <th>26.320356</th>\n",
       "    <th>0.768566</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>733</th>\n",
       "    <th>16.333149</th>\n",
       "    <th>26.332781</th>\n",
       "    <th>26.332781</th>\n",
       "    <th>0.781083</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>734</th>\n",
       "    <th>16.304398</th>\n",
       "    <th>26.629656</th>\n",
       "    <th>26.629656</th>\n",
       "    <th>0.776172</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>735</th>\n",
       "    <th>16.351761</th>\n",
       "    <th>26.996838</th>\n",
       "    <th>26.996838</th>\n",
       "    <th>0.784567</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>736</th>\n",
       "    <th>16.177979</th>\n",
       "    <th>27.498106</th>\n",
       "    <th>27.498106</th>\n",
       "    <th>0.771815</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>737</th>\n",
       "    <th>16.145006</th>\n",
       "    <th>26.042385</th>\n",
       "    <th>26.042385</th>\n",
       "    <th>0.781434</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>738</th>\n",
       "    <th>16.243219</th>\n",
       "    <th>25.886551</th>\n",
       "    <th>25.886551</th>\n",
       "    <th>0.776044</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>739</th>\n",
       "    <th>16.227554</th>\n",
       "    <th>26.875969</th>\n",
       "    <th>26.875969</th>\n",
       "    <th>0.774527</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>740</th>\n",
       "    <th>16.427404</th>\n",
       "    <th>27.457100</th>\n",
       "    <th>27.457100</th>\n",
       "    <th>0.797110</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>741</th>\n",
       "    <th>16.470591</th>\n",
       "    <th>26.827768</th>\n",
       "    <th>26.827768</th>\n",
       "    <th>0.751330</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>742</th>\n",
       "    <th>16.480015</th>\n",
       "    <th>26.869232</th>\n",
       "    <th>26.869232</th>\n",
       "    <th>0.775285</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>743</th>\n",
       "    <th>16.256559</th>\n",
       "    <th>26.383507</th>\n",
       "    <th>26.383507</th>\n",
       "    <th>0.789651</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>744</th>\n",
       "    <th>16.184774</th>\n",
       "    <th>27.275856</th>\n",
       "    <th>27.275856</th>\n",
       "    <th>0.773289</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>745</th>\n",
       "    <th>16.090593</th>\n",
       "    <th>26.560165</th>\n",
       "    <th>26.560165</th>\n",
       "    <th>0.763087</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>746</th>\n",
       "    <th>16.021952</th>\n",
       "    <th>25.742140</th>\n",
       "    <th>25.742140</th>\n",
       "    <th>0.778315</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>747</th>\n",
       "    <th>16.058233</th>\n",
       "    <th>26.444187</th>\n",
       "    <th>26.444187</th>\n",
       "    <th>0.772385</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>748</th>\n",
       "    <th>16.024609</th>\n",
       "    <th>25.981207</th>\n",
       "    <th>25.981207</th>\n",
       "    <th>0.773159</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>749</th>\n",
       "    <th>16.122517</th>\n",
       "    <th>26.775326</th>\n",
       "    <th>26.775326</th>\n",
       "    <th>0.771879</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>750</th>\n",
       "    <th>16.199995</th>\n",
       "    <th>27.860903</th>\n",
       "    <th>27.860903</th>\n",
       "    <th>0.776436</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>751</th>\n",
       "    <th>16.101187</th>\n",
       "    <th>26.849121</th>\n",
       "    <th>26.849121</th>\n",
       "    <th>0.758433</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>752</th>\n",
       "    <th>16.223288</th>\n",
       "    <th>27.028397</th>\n",
       "    <th>27.028397</th>\n",
       "    <th>0.768024</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>753</th>\n",
       "    <th>16.100479</th>\n",
       "    <th>26.762060</th>\n",
       "    <th>26.762060</th>\n",
       "    <th>0.762473</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>754</th>\n",
       "    <th>16.098104</th>\n",
       "    <th>26.657082</th>\n",
       "    <th>26.657082</th>\n",
       "    <th>0.765001</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>755</th>\n",
       "    <th>16.112614</th>\n",
       "    <th>26.652925</th>\n",
       "    <th>26.652925</th>\n",
       "    <th>0.769466</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>756</th>\n",
       "    <th>16.138206</th>\n",
       "    <th>26.777266</th>\n",
       "    <th>26.777266</th>\n",
       "    <th>0.776851</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>757</th>\n",
       "    <th>16.139540</th>\n",
       "    <th>27.609327</th>\n",
       "    <th>27.609327</th>\n",
       "    <th>0.783006</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>758</th>\n",
       "    <th>15.935586</th>\n",
       "    <th>26.541197</th>\n",
       "    <th>26.541197</th>\n",
       "    <th>0.774889</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>759</th>\n",
       "    <th>16.008770</th>\n",
       "    <th>26.247675</th>\n",
       "    <th>26.247675</th>\n",
       "    <th>0.773949</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>760</th>\n",
       "    <th>15.976918</th>\n",
       "    <th>27.615732</th>\n",
       "    <th>27.615732</th>\n",
       "    <th>0.777226</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>761</th>\n",
       "    <th>15.912204</th>\n",
       "    <th>27.020388</th>\n",
       "    <th>27.020388</th>\n",
       "    <th>0.780458</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>762</th>\n",
       "    <th>15.975830</th>\n",
       "    <th>26.805853</th>\n",
       "    <th>26.805853</th>\n",
       "    <th>0.770869</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>763</th>\n",
       "    <th>16.064793</th>\n",
       "    <th>26.933632</th>\n",
       "    <th>26.933632</th>\n",
       "    <th>0.775164</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>764</th>\n",
       "    <th>16.077040</th>\n",
       "    <th>25.477722</th>\n",
       "    <th>25.477722</th>\n",
       "    <th>0.766418</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>765</th>\n",
       "    <th>16.114542</th>\n",
       "    <th>27.833401</th>\n",
       "    <th>27.833401</th>\n",
       "    <th>0.807746</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>766</th>\n",
       "    <th>15.978087</th>\n",
       "    <th>26.363806</th>\n",
       "    <th>26.363806</th>\n",
       "    <th>0.783080</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>767</th>\n",
       "    <th>15.952321</th>\n",
       "    <th>27.403112</th>\n",
       "    <th>27.403112</th>\n",
       "    <th>0.781168</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>768</th>\n",
       "    <th>15.961365</th>\n",
       "    <th>26.561745</th>\n",
       "    <th>26.561745</th>\n",
       "    <th>0.784724</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>769</th>\n",
       "    <th>15.934247</th>\n",
       "    <th>27.736622</th>\n",
       "    <th>27.736622</th>\n",
       "    <th>0.779042</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>770</th>\n",
       "    <th>16.057646</th>\n",
       "    <th>26.650335</th>\n",
       "    <th>26.650335</th>\n",
       "    <th>0.767209</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>771</th>\n",
       "    <th>15.912271</th>\n",
       "    <th>26.600901</th>\n",
       "    <th>26.600901</th>\n",
       "    <th>0.765218</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>772</th>\n",
       "    <th>15.917128</th>\n",
       "    <th>26.307190</th>\n",
       "    <th>26.307190</th>\n",
       "    <th>0.766248</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>773</th>\n",
       "    <th>15.875853</th>\n",
       "    <th>26.584940</th>\n",
       "    <th>26.584940</th>\n",
       "    <th>0.790677</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>774</th>\n",
       "    <th>15.957987</th>\n",
       "    <th>25.605068</th>\n",
       "    <th>25.605068</th>\n",
       "    <th>0.781526</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>775</th>\n",
       "    <th>15.988730</th>\n",
       "    <th>26.554781</th>\n",
       "    <th>26.554781</th>\n",
       "    <th>0.773721</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>776</th>\n",
       "    <th>15.951944</th>\n",
       "    <th>26.476360</th>\n",
       "    <th>26.476360</th>\n",
       "    <th>0.771809</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>777</th>\n",
       "    <th>16.212246</th>\n",
       "    <th>26.844509</th>\n",
       "    <th>26.844509</th>\n",
       "    <th>0.774460</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>778</th>\n",
       "    <th>15.923537</th>\n",
       "    <th>26.465935</th>\n",
       "    <th>26.465935</th>\n",
       "    <th>0.760306</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>779</th>\n",
       "    <th>15.822569</th>\n",
       "    <th>26.181179</th>\n",
       "    <th>26.181179</th>\n",
       "    <th>0.795901</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>780</th>\n",
       "    <th>15.759483</th>\n",
       "    <th>25.476515</th>\n",
       "    <th>25.476515</th>\n",
       "    <th>0.779258</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>781</th>\n",
       "    <th>15.903215</th>\n",
       "    <th>26.954203</th>\n",
       "    <th>26.954203</th>\n",
       "    <th>0.785558</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>782</th>\n",
       "    <th>15.891398</th>\n",
       "    <th>26.502218</th>\n",
       "    <th>26.502218</th>\n",
       "    <th>0.774113</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>783</th>\n",
       "    <th>15.798021</th>\n",
       "    <th>26.490009</th>\n",
       "    <th>26.490009</th>\n",
       "    <th>0.770469</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>784</th>\n",
       "    <th>15.917471</th>\n",
       "    <th>27.103304</th>\n",
       "    <th>27.103304</th>\n",
       "    <th>0.785899</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>785</th>\n",
       "    <th>15.883762</th>\n",
       "    <th>26.827518</th>\n",
       "    <th>26.827518</th>\n",
       "    <th>0.773131</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>786</th>\n",
       "    <th>15.868476</th>\n",
       "    <th>26.081078</th>\n",
       "    <th>26.081078</th>\n",
       "    <th>0.756615</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>787</th>\n",
       "    <th>15.878188</th>\n",
       "    <th>27.404028</th>\n",
       "    <th>27.404028</th>\n",
       "    <th>0.789692</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>788</th>\n",
       "    <th>15.869326</th>\n",
       "    <th>26.691887</th>\n",
       "    <th>26.691887</th>\n",
       "    <th>0.780338</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>789</th>\n",
       "    <th>15.813388</th>\n",
       "    <th>27.333284</th>\n",
       "    <th>27.333284</th>\n",
       "    <th>0.780958</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>790</th>\n",
       "    <th>15.848402</th>\n",
       "    <th>26.867725</th>\n",
       "    <th>26.867725</th>\n",
       "    <th>0.781186</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>791</th>\n",
       "    <th>15.757720</th>\n",
       "    <th>27.145910</th>\n",
       "    <th>27.145910</th>\n",
       "    <th>0.777919</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>792</th>\n",
       "    <th>15.848384</th>\n",
       "    <th>26.710726</th>\n",
       "    <th>26.710726</th>\n",
       "    <th>0.775910</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>793</th>\n",
       "    <th>15.858904</th>\n",
       "    <th>27.033859</th>\n",
       "    <th>27.033859</th>\n",
       "    <th>0.782309</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>794</th>\n",
       "    <th>15.830269</th>\n",
       "    <th>26.253613</th>\n",
       "    <th>26.253613</th>\n",
       "    <th>0.772062</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>795</th>\n",
       "    <th>15.767147</th>\n",
       "    <th>27.490437</th>\n",
       "    <th>27.490437</th>\n",
       "    <th>0.781337</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>796</th>\n",
       "    <th>15.689296</th>\n",
       "    <th>25.921278</th>\n",
       "    <th>25.921278</th>\n",
       "    <th>0.756804</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>797</th>\n",
       "    <th>15.809246</th>\n",
       "    <th>26.519070</th>\n",
       "    <th>26.519070</th>\n",
       "    <th>0.771637</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>798</th>\n",
       "    <th>15.612246</th>\n",
       "    <th>27.362394</th>\n",
       "    <th>27.362394</th>\n",
       "    <th>0.777916</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>799</th>\n",
       "    <th>15.626660</th>\n",
       "    <th>26.932716</th>\n",
       "    <th>26.932716</th>\n",
       "    <th>0.773762</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>800</th>\n",
       "    <th>15.739276</th>\n",
       "    <th>26.871332</th>\n",
       "    <th>26.871332</th>\n",
       "    <th>0.775730</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>801</th>\n",
       "    <th>15.553497</th>\n",
       "    <th>27.267010</th>\n",
       "    <th>27.267010</th>\n",
       "    <th>0.774637</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>802</th>\n",
       "    <th>15.732297</th>\n",
       "    <th>26.435997</th>\n",
       "    <th>26.435997</th>\n",
       "    <th>0.761253</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>803</th>\n",
       "    <th>15.778193</th>\n",
       "    <th>25.592674</th>\n",
       "    <th>25.592674</th>\n",
       "    <th>0.767730</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>804</th>\n",
       "    <th>15.648088</th>\n",
       "    <th>26.474546</th>\n",
       "    <th>26.474546</th>\n",
       "    <th>0.768816</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>805</th>\n",
       "    <th>15.756446</th>\n",
       "    <th>27.655678</th>\n",
       "    <th>27.655678</th>\n",
       "    <th>0.764608</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>806</th>\n",
       "    <th>15.728832</th>\n",
       "    <th>26.526302</th>\n",
       "    <th>26.526302</th>\n",
       "    <th>0.780313</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>807</th>\n",
       "    <th>15.600104</th>\n",
       "    <th>26.698532</th>\n",
       "    <th>26.698532</th>\n",
       "    <th>0.786654</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>808</th>\n",
       "    <th>15.687263</th>\n",
       "    <th>26.561882</th>\n",
       "    <th>26.561882</th>\n",
       "    <th>0.776481</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>809</th>\n",
       "    <th>15.653159</th>\n",
       "    <th>26.402252</th>\n",
       "    <th>26.402252</th>\n",
       "    <th>0.775408</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>810</th>\n",
       "    <th>15.615653</th>\n",
       "    <th>26.479670</th>\n",
       "    <th>26.479670</th>\n",
       "    <th>0.768536</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>811</th>\n",
       "    <th>15.590000</th>\n",
       "    <th>25.789648</th>\n",
       "    <th>25.789648</th>\n",
       "    <th>0.764453</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>812</th>\n",
       "    <th>15.630110</th>\n",
       "    <th>26.956966</th>\n",
       "    <th>26.956966</th>\n",
       "    <th>0.779051</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>813</th>\n",
       "    <th>15.578461</th>\n",
       "    <th>26.098646</th>\n",
       "    <th>26.098646</th>\n",
       "    <th>0.781814</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>814</th>\n",
       "    <th>15.610762</th>\n",
       "    <th>26.235001</th>\n",
       "    <th>26.235001</th>\n",
       "    <th>0.790847</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>815</th>\n",
       "    <th>15.712774</th>\n",
       "    <th>27.292425</th>\n",
       "    <th>27.292425</th>\n",
       "    <th>0.780399</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>816</th>\n",
       "    <th>15.576017</th>\n",
       "    <th>26.226913</th>\n",
       "    <th>26.226913</th>\n",
       "    <th>0.777625</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>817</th>\n",
       "    <th>15.525350</th>\n",
       "    <th>26.016218</th>\n",
       "    <th>26.016218</th>\n",
       "    <th>0.763312</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>818</th>\n",
       "    <th>15.622389</th>\n",
       "    <th>26.284916</th>\n",
       "    <th>26.284916</th>\n",
       "    <th>0.780027</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>819</th>\n",
       "    <th>15.473096</th>\n",
       "    <th>26.509066</th>\n",
       "    <th>26.509066</th>\n",
       "    <th>0.773300</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>820</th>\n",
       "    <th>15.627562</th>\n",
       "    <th>26.498672</th>\n",
       "    <th>26.498672</th>\n",
       "    <th>0.762684</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>821</th>\n",
       "    <th>15.457737</th>\n",
       "    <th>26.433666</th>\n",
       "    <th>26.433666</th>\n",
       "    <th>0.765965</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>822</th>\n",
       "    <th>15.504725</th>\n",
       "    <th>26.063087</th>\n",
       "    <th>26.063087</th>\n",
       "    <th>0.768126</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>823</th>\n",
       "    <th>15.622001</th>\n",
       "    <th>26.656797</th>\n",
       "    <th>26.656797</th>\n",
       "    <th>0.767439</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>824</th>\n",
       "    <th>15.500531</th>\n",
       "    <th>26.718193</th>\n",
       "    <th>26.718193</th>\n",
       "    <th>0.778067</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>825</th>\n",
       "    <th>15.504116</th>\n",
       "    <th>26.512033</th>\n",
       "    <th>26.512033</th>\n",
       "    <th>0.772212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>826</th>\n",
       "    <th>15.501362</th>\n",
       "    <th>26.731855</th>\n",
       "    <th>26.731855</th>\n",
       "    <th>0.770807</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>827</th>\n",
       "    <th>15.465287</th>\n",
       "    <th>26.244448</th>\n",
       "    <th>26.244448</th>\n",
       "    <th>0.770516</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>828</th>\n",
       "    <th>15.463637</th>\n",
       "    <th>25.621935</th>\n",
       "    <th>25.621935</th>\n",
       "    <th>0.773873</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>829</th>\n",
       "    <th>15.432474</th>\n",
       "    <th>26.415937</th>\n",
       "    <th>26.415937</th>\n",
       "    <th>0.768766</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>830</th>\n",
       "    <th>15.442280</th>\n",
       "    <th>26.933681</th>\n",
       "    <th>26.933681</th>\n",
       "    <th>0.774400</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>831</th>\n",
       "    <th>15.339872</th>\n",
       "    <th>26.651478</th>\n",
       "    <th>26.651478</th>\n",
       "    <th>0.777823</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>832</th>\n",
       "    <th>15.435295</th>\n",
       "    <th>26.660843</th>\n",
       "    <th>26.660843</th>\n",
       "    <th>0.770647</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>833</th>\n",
       "    <th>15.457171</th>\n",
       "    <th>26.635387</th>\n",
       "    <th>26.635387</th>\n",
       "    <th>0.775251</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>834</th>\n",
       "    <th>15.444793</th>\n",
       "    <th>26.120394</th>\n",
       "    <th>26.120394</th>\n",
       "    <th>0.765832</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>835</th>\n",
       "    <th>15.504260</th>\n",
       "    <th>27.076946</th>\n",
       "    <th>27.076946</th>\n",
       "    <th>0.773759</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>836</th>\n",
       "    <th>15.342585</th>\n",
       "    <th>29.578966</th>\n",
       "    <th>29.578966</th>\n",
       "    <th>0.749917</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>837</th>\n",
       "    <th>15.324944</th>\n",
       "    <th>26.758537</th>\n",
       "    <th>26.758537</th>\n",
       "    <th>0.772204</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>838</th>\n",
       "    <th>15.392042</th>\n",
       "    <th>25.930836</th>\n",
       "    <th>25.930836</th>\n",
       "    <th>0.775553</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>839</th>\n",
       "    <th>15.541780</th>\n",
       "    <th>26.423426</th>\n",
       "    <th>26.423426</th>\n",
       "    <th>0.771063</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>840</th>\n",
       "    <th>15.360236</th>\n",
       "    <th>26.230059</th>\n",
       "    <th>26.230059</th>\n",
       "    <th>0.766175</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>841</th>\n",
       "    <th>15.411976</th>\n",
       "    <th>26.668459</th>\n",
       "    <th>26.668459</th>\n",
       "    <th>0.776887</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>842</th>\n",
       "    <th>15.323850</th>\n",
       "    <th>26.229416</th>\n",
       "    <th>26.229416</th>\n",
       "    <th>0.774909</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>843</th>\n",
       "    <th>15.258254</th>\n",
       "    <th>26.354195</th>\n",
       "    <th>26.354195</th>\n",
       "    <th>0.773224</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>844</th>\n",
       "    <th>15.414723</th>\n",
       "    <th>26.319387</th>\n",
       "    <th>26.319387</th>\n",
       "    <th>0.765693</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>845</th>\n",
       "    <th>15.415730</th>\n",
       "    <th>26.778219</th>\n",
       "    <th>26.778219</th>\n",
       "    <th>0.773370</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>846</th>\n",
       "    <th>15.345242</th>\n",
       "    <th>26.112158</th>\n",
       "    <th>26.112158</th>\n",
       "    <th>0.771677</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>847</th>\n",
       "    <th>15.146541</th>\n",
       "    <th>26.217497</th>\n",
       "    <th>26.217497</th>\n",
       "    <th>0.779408</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>848</th>\n",
       "    <th>15.152853</th>\n",
       "    <th>26.474016</th>\n",
       "    <th>26.474016</th>\n",
       "    <th>0.780850</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>849</th>\n",
       "    <th>15.249950</th>\n",
       "    <th>26.404160</th>\n",
       "    <th>26.404160</th>\n",
       "    <th>0.776551</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>850</th>\n",
       "    <th>15.263223</th>\n",
       "    <th>27.097631</th>\n",
       "    <th>27.097631</th>\n",
       "    <th>0.765309</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>851</th>\n",
       "    <th>15.253131</th>\n",
       "    <th>27.135666</th>\n",
       "    <th>27.135666</th>\n",
       "    <th>0.771293</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>852</th>\n",
       "    <th>15.323964</th>\n",
       "    <th>26.593025</th>\n",
       "    <th>26.593025</th>\n",
       "    <th>0.778709</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>853</th>\n",
       "    <th>15.479769</th>\n",
       "    <th>25.932997</th>\n",
       "    <th>25.932997</th>\n",
       "    <th>0.770650</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>854</th>\n",
       "    <th>15.323648</th>\n",
       "    <th>26.941601</th>\n",
       "    <th>26.941601</th>\n",
       "    <th>0.761748</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>855</th>\n",
       "    <th>15.248585</th>\n",
       "    <th>26.604244</th>\n",
       "    <th>26.604244</th>\n",
       "    <th>0.771984</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>856</th>\n",
       "    <th>15.274746</th>\n",
       "    <th>26.498062</th>\n",
       "    <th>26.498062</th>\n",
       "    <th>0.781054</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>857</th>\n",
       "    <th>15.190885</th>\n",
       "    <th>26.043215</th>\n",
       "    <th>26.043215</th>\n",
       "    <th>0.765825</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>858</th>\n",
       "    <th>15.320936</th>\n",
       "    <th>25.524202</th>\n",
       "    <th>25.524202</th>\n",
       "    <th>0.771129</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>859</th>\n",
       "    <th>15.278631</th>\n",
       "    <th>25.968004</th>\n",
       "    <th>25.968004</th>\n",
       "    <th>0.773435</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>860</th>\n",
       "    <th>15.138772</th>\n",
       "    <th>26.711115</th>\n",
       "    <th>26.711115</th>\n",
       "    <th>0.769209</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>861</th>\n",
       "    <th>15.235273</th>\n",
       "    <th>26.889940</th>\n",
       "    <th>26.889940</th>\n",
       "    <th>0.765173</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>862</th>\n",
       "    <th>15.349152</th>\n",
       "    <th>26.244041</th>\n",
       "    <th>26.244041</th>\n",
       "    <th>0.767798</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>863</th>\n",
       "    <th>15.209461</th>\n",
       "    <th>26.058702</th>\n",
       "    <th>26.058702</th>\n",
       "    <th>0.769993</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>864</th>\n",
       "    <th>15.303579</th>\n",
       "    <th>26.609806</th>\n",
       "    <th>26.609806</th>\n",
       "    <th>0.776001</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>865</th>\n",
       "    <th>15.103242</th>\n",
       "    <th>26.024170</th>\n",
       "    <th>26.024170</th>\n",
       "    <th>0.763487</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>866</th>\n",
       "    <th>15.087253</th>\n",
       "    <th>26.280622</th>\n",
       "    <th>26.280622</th>\n",
       "    <th>0.761954</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>867</th>\n",
       "    <th>15.112164</th>\n",
       "    <th>25.619442</th>\n",
       "    <th>25.619442</th>\n",
       "    <th>0.774323</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>868</th>\n",
       "    <th>15.023764</th>\n",
       "    <th>26.723558</th>\n",
       "    <th>26.723558</th>\n",
       "    <th>0.772892</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>869</th>\n",
       "    <th>15.080664</th>\n",
       "    <th>26.721676</th>\n",
       "    <th>26.721676</th>\n",
       "    <th>0.776281</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>870</th>\n",
       "    <th>15.221359</th>\n",
       "    <th>25.895990</th>\n",
       "    <th>25.895990</th>\n",
       "    <th>0.772445</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>871</th>\n",
       "    <th>15.104423</th>\n",
       "    <th>26.505360</th>\n",
       "    <th>26.505360</th>\n",
       "    <th>0.761184</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>872</th>\n",
       "    <th>15.048839</th>\n",
       "    <th>26.357937</th>\n",
       "    <th>26.357937</th>\n",
       "    <th>0.767822</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>873</th>\n",
       "    <th>15.192492</th>\n",
       "    <th>26.211548</th>\n",
       "    <th>26.211548</th>\n",
       "    <th>0.762536</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>874</th>\n",
       "    <th>15.220163</th>\n",
       "    <th>26.675463</th>\n",
       "    <th>26.675463</th>\n",
       "    <th>0.771340</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>875</th>\n",
       "    <th>15.033642</th>\n",
       "    <th>26.367168</th>\n",
       "    <th>26.367168</th>\n",
       "    <th>0.762953</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>876</th>\n",
       "    <th>14.980660</th>\n",
       "    <th>25.513611</th>\n",
       "    <th>25.513611</th>\n",
       "    <th>0.762231</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>877</th>\n",
       "    <th>15.059268</th>\n",
       "    <th>26.558369</th>\n",
       "    <th>26.558369</th>\n",
       "    <th>0.763111</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>878</th>\n",
       "    <th>15.200789</th>\n",
       "    <th>26.113510</th>\n",
       "    <th>26.113510</th>\n",
       "    <th>0.768337</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>879</th>\n",
       "    <th>15.183466</th>\n",
       "    <th>27.229759</th>\n",
       "    <th>27.229759</th>\n",
       "    <th>0.779053</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>880</th>\n",
       "    <th>15.066158</th>\n",
       "    <th>26.352047</th>\n",
       "    <th>26.352047</th>\n",
       "    <th>0.769894</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>881</th>\n",
       "    <th>15.150361</th>\n",
       "    <th>26.395330</th>\n",
       "    <th>26.395330</th>\n",
       "    <th>0.762173</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>882</th>\n",
       "    <th>15.263481</th>\n",
       "    <th>26.777597</th>\n",
       "    <th>26.777597</th>\n",
       "    <th>0.764864</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>883</th>\n",
       "    <th>15.134683</th>\n",
       "    <th>26.578947</th>\n",
       "    <th>26.578947</th>\n",
       "    <th>0.756825</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>884</th>\n",
       "    <th>15.205727</th>\n",
       "    <th>26.322357</th>\n",
       "    <th>26.322357</th>\n",
       "    <th>0.774777</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>885</th>\n",
       "    <th>15.017532</th>\n",
       "    <th>26.915415</th>\n",
       "    <th>26.915415</th>\n",
       "    <th>0.780112</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>886</th>\n",
       "    <th>15.000522</th>\n",
       "    <th>26.114132</th>\n",
       "    <th>26.114132</th>\n",
       "    <th>0.754799</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>887</th>\n",
       "    <th>14.989141</th>\n",
       "    <th>26.672441</th>\n",
       "    <th>26.672441</th>\n",
       "    <th>0.761706</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>888</th>\n",
       "    <th>15.047207</th>\n",
       "    <th>26.444288</th>\n",
       "    <th>26.444288</th>\n",
       "    <th>0.769196</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>889</th>\n",
       "    <th>15.111229</th>\n",
       "    <th>26.396904</th>\n",
       "    <th>26.396904</th>\n",
       "    <th>0.773254</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>890</th>\n",
       "    <th>15.042937</th>\n",
       "    <th>26.844343</th>\n",
       "    <th>26.844343</th>\n",
       "    <th>0.757280</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>891</th>\n",
       "    <th>15.038696</th>\n",
       "    <th>25.795914</th>\n",
       "    <th>25.795914</th>\n",
       "    <th>0.767550</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>892</th>\n",
       "    <th>14.922998</th>\n",
       "    <th>26.602615</th>\n",
       "    <th>26.602615</th>\n",
       "    <th>0.780787</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>893</th>\n",
       "    <th>14.952787</th>\n",
       "    <th>26.741560</th>\n",
       "    <th>26.741560</th>\n",
       "    <th>0.771624</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>894</th>\n",
       "    <th>15.011549</th>\n",
       "    <th>26.906147</th>\n",
       "    <th>26.906147</th>\n",
       "    <th>0.771165</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>895</th>\n",
       "    <th>14.793776</th>\n",
       "    <th>26.626272</th>\n",
       "    <th>26.626272</th>\n",
       "    <th>0.778084</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>896</th>\n",
       "    <th>14.897205</th>\n",
       "    <th>26.742716</th>\n",
       "    <th>26.742716</th>\n",
       "    <th>0.767618</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>897</th>\n",
       "    <th>14.842381</th>\n",
       "    <th>26.190857</th>\n",
       "    <th>26.190857</th>\n",
       "    <th>0.771402</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>898</th>\n",
       "    <th>14.909669</th>\n",
       "    <th>26.260878</th>\n",
       "    <th>26.260878</th>\n",
       "    <th>0.767538</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>899</th>\n",
       "    <th>15.061179</th>\n",
       "    <th>26.685013</th>\n",
       "    <th>26.685013</th>\n",
       "    <th>0.771333</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>900</th>\n",
       "    <th>14.914935</th>\n",
       "    <th>26.663534</th>\n",
       "    <th>26.663534</th>\n",
       "    <th>0.763764</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>901</th>\n",
       "    <th>14.909258</th>\n",
       "    <th>26.583366</th>\n",
       "    <th>26.583366</th>\n",
       "    <th>0.771456</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>902</th>\n",
       "    <th>14.865605</th>\n",
       "    <th>25.484888</th>\n",
       "    <th>25.484888</th>\n",
       "    <th>0.761740</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>903</th>\n",
       "    <th>14.830711</th>\n",
       "    <th>26.395168</th>\n",
       "    <th>26.395168</th>\n",
       "    <th>0.768831</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>904</th>\n",
       "    <th>14.819597</th>\n",
       "    <th>25.340195</th>\n",
       "    <th>25.340195</th>\n",
       "    <th>0.763898</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>905</th>\n",
       "    <th>14.817557</th>\n",
       "    <th>26.228535</th>\n",
       "    <th>26.228535</th>\n",
       "    <th>0.773991</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>906</th>\n",
       "    <th>14.871049</th>\n",
       "    <th>26.401865</th>\n",
       "    <th>26.401865</th>\n",
       "    <th>0.766198</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>907</th>\n",
       "    <th>14.872093</th>\n",
       "    <th>26.361290</th>\n",
       "    <th>26.361290</th>\n",
       "    <th>0.765965</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>908</th>\n",
       "    <th>14.962419</th>\n",
       "    <th>26.507715</th>\n",
       "    <th>26.507715</th>\n",
       "    <th>0.771497</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>909</th>\n",
       "    <th>14.805143</th>\n",
       "    <th>26.684504</th>\n",
       "    <th>26.684504</th>\n",
       "    <th>0.772091</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>910</th>\n",
       "    <th>14.937850</th>\n",
       "    <th>26.445356</th>\n",
       "    <th>26.445356</th>\n",
       "    <th>0.769056</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>911</th>\n",
       "    <th>14.881841</th>\n",
       "    <th>27.022293</th>\n",
       "    <th>27.022293</th>\n",
       "    <th>0.771372</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>912</th>\n",
       "    <th>14.948468</th>\n",
       "    <th>26.530130</th>\n",
       "    <th>26.530130</th>\n",
       "    <th>0.764599</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>913</th>\n",
       "    <th>14.869398</th>\n",
       "    <th>26.506565</th>\n",
       "    <th>26.506565</th>\n",
       "    <th>0.777719</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>914</th>\n",
       "    <th>14.796181</th>\n",
       "    <th>25.876715</th>\n",
       "    <th>25.876715</th>\n",
       "    <th>0.773216</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>915</th>\n",
       "    <th>14.902122</th>\n",
       "    <th>26.087467</th>\n",
       "    <th>26.087467</th>\n",
       "    <th>0.772060</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>916</th>\n",
       "    <th>14.997400</th>\n",
       "    <th>26.759241</th>\n",
       "    <th>26.759241</th>\n",
       "    <th>0.763390</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>917</th>\n",
       "    <th>14.868496</th>\n",
       "    <th>26.264172</th>\n",
       "    <th>26.264172</th>\n",
       "    <th>0.766206</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>918</th>\n",
       "    <th>14.837629</th>\n",
       "    <th>26.463331</th>\n",
       "    <th>26.463331</th>\n",
       "    <th>0.767795</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>919</th>\n",
       "    <th>14.776118</th>\n",
       "    <th>26.578959</th>\n",
       "    <th>26.578959</th>\n",
       "    <th>0.772647</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>920</th>\n",
       "    <th>14.854865</th>\n",
       "    <th>26.705505</th>\n",
       "    <th>26.705505</th>\n",
       "    <th>0.768210</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>921</th>\n",
       "    <th>14.792645</th>\n",
       "    <th>25.380434</th>\n",
       "    <th>25.380434</th>\n",
       "    <th>0.767146</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>922</th>\n",
       "    <th>14.751074</th>\n",
       "    <th>26.602110</th>\n",
       "    <th>26.602110</th>\n",
       "    <th>0.765767</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>923</th>\n",
       "    <th>14.639127</th>\n",
       "    <th>26.179001</th>\n",
       "    <th>26.179001</th>\n",
       "    <th>0.764025</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>924</th>\n",
       "    <th>14.719211</th>\n",
       "    <th>26.305485</th>\n",
       "    <th>26.305485</th>\n",
       "    <th>0.760813</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>925</th>\n",
       "    <th>14.725826</th>\n",
       "    <th>26.240660</th>\n",
       "    <th>26.240660</th>\n",
       "    <th>0.768705</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>926</th>\n",
       "    <th>14.751491</th>\n",
       "    <th>26.079031</th>\n",
       "    <th>26.079031</th>\n",
       "    <th>0.766485</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>927</th>\n",
       "    <th>14.837001</th>\n",
       "    <th>26.712978</th>\n",
       "    <th>26.712978</th>\n",
       "    <th>0.770767</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>928</th>\n",
       "    <th>14.732553</th>\n",
       "    <th>25.821121</th>\n",
       "    <th>25.821121</th>\n",
       "    <th>0.769708</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>929</th>\n",
       "    <th>14.671622</th>\n",
       "    <th>26.156881</th>\n",
       "    <th>26.156881</th>\n",
       "    <th>0.763715</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>930</th>\n",
       "    <th>14.810900</th>\n",
       "    <th>26.608782</th>\n",
       "    <th>26.608782</th>\n",
       "    <th>0.773018</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>931</th>\n",
       "    <th>14.667996</th>\n",
       "    <th>25.959478</th>\n",
       "    <th>25.959478</th>\n",
       "    <th>0.766309</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>932</th>\n",
       "    <th>14.618654</th>\n",
       "    <th>25.954304</th>\n",
       "    <th>25.954304</th>\n",
       "    <th>0.762759</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>933</th>\n",
       "    <th>14.662347</th>\n",
       "    <th>26.214560</th>\n",
       "    <th>26.214560</th>\n",
       "    <th>0.763244</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>934</th>\n",
       "    <th>14.723607</th>\n",
       "    <th>26.154949</th>\n",
       "    <th>26.154949</th>\n",
       "    <th>0.774118</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>935</th>\n",
       "    <th>14.583378</th>\n",
       "    <th>26.662588</th>\n",
       "    <th>26.662588</th>\n",
       "    <th>0.765445</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>936</th>\n",
       "    <th>14.590559</th>\n",
       "    <th>26.298710</th>\n",
       "    <th>26.298710</th>\n",
       "    <th>0.769889</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>937</th>\n",
       "    <th>14.763989</th>\n",
       "    <th>26.060085</th>\n",
       "    <th>26.060085</th>\n",
       "    <th>0.777838</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>938</th>\n",
       "    <th>14.626262</th>\n",
       "    <th>25.951107</th>\n",
       "    <th>25.951107</th>\n",
       "    <th>0.765182</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>939</th>\n",
       "    <th>14.678647</th>\n",
       "    <th>26.260332</th>\n",
       "    <th>26.260332</th>\n",
       "    <th>0.763147</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>940</th>\n",
       "    <th>14.736262</th>\n",
       "    <th>26.365904</th>\n",
       "    <th>26.365904</th>\n",
       "    <th>0.780919</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>941</th>\n",
       "    <th>14.615879</th>\n",
       "    <th>26.174433</th>\n",
       "    <th>26.174433</th>\n",
       "    <th>0.760489</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>942</th>\n",
       "    <th>14.624190</th>\n",
       "    <th>26.140871</th>\n",
       "    <th>26.140871</th>\n",
       "    <th>0.769628</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>943</th>\n",
       "    <th>14.669779</th>\n",
       "    <th>25.677864</th>\n",
       "    <th>25.677864</th>\n",
       "    <th>0.756956</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>944</th>\n",
       "    <th>14.622766</th>\n",
       "    <th>26.393826</th>\n",
       "    <th>26.393826</th>\n",
       "    <th>0.772715</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>945</th>\n",
       "    <th>14.573943</th>\n",
       "    <th>25.907183</th>\n",
       "    <th>25.907183</th>\n",
       "    <th>0.765290</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>946</th>\n",
       "    <th>14.563412</th>\n",
       "    <th>25.872723</th>\n",
       "    <th>25.872723</th>\n",
       "    <th>0.774957</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>947</th>\n",
       "    <th>14.605337</th>\n",
       "    <th>25.795694</th>\n",
       "    <th>25.795694</th>\n",
       "    <th>0.758789</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>948</th>\n",
       "    <th>14.826303</th>\n",
       "    <th>26.478041</th>\n",
       "    <th>26.478041</th>\n",
       "    <th>0.765052</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>949</th>\n",
       "    <th>14.631048</th>\n",
       "    <th>25.967087</th>\n",
       "    <th>25.967087</th>\n",
       "    <th>0.763228</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>950</th>\n",
       "    <th>14.552586</th>\n",
       "    <th>26.431807</th>\n",
       "    <th>26.431807</th>\n",
       "    <th>0.760009</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>951</th>\n",
       "    <th>14.613959</th>\n",
       "    <th>26.123526</th>\n",
       "    <th>26.123526</th>\n",
       "    <th>0.766586</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>952</th>\n",
       "    <th>14.585486</th>\n",
       "    <th>26.117085</th>\n",
       "    <th>26.117085</th>\n",
       "    <th>0.776413</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>953</th>\n",
       "    <th>14.551595</th>\n",
       "    <th>26.376616</th>\n",
       "    <th>26.376616</th>\n",
       "    <th>0.753432</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>954</th>\n",
       "    <th>14.606125</th>\n",
       "    <th>26.210516</th>\n",
       "    <th>26.210516</th>\n",
       "    <th>0.763841</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>955</th>\n",
       "    <th>14.606129</th>\n",
       "    <th>26.337288</th>\n",
       "    <th>26.337288</th>\n",
       "    <th>0.760398</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>956</th>\n",
       "    <th>14.516741</th>\n",
       "    <th>26.112267</th>\n",
       "    <th>26.112267</th>\n",
       "    <th>0.769394</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>957</th>\n",
       "    <th>14.538574</th>\n",
       "    <th>25.820147</th>\n",
       "    <th>25.820147</th>\n",
       "    <th>0.754062</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>958</th>\n",
       "    <th>14.462829</th>\n",
       "    <th>26.061684</th>\n",
       "    <th>26.061684</th>\n",
       "    <th>0.766202</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>959</th>\n",
       "    <th>14.569968</th>\n",
       "    <th>26.148010</th>\n",
       "    <th>26.148010</th>\n",
       "    <th>0.768493</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>960</th>\n",
       "    <th>14.522357</th>\n",
       "    <th>26.093582</th>\n",
       "    <th>26.093582</th>\n",
       "    <th>0.777641</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>961</th>\n",
       "    <th>14.563293</th>\n",
       "    <th>26.289772</th>\n",
       "    <th>26.289772</th>\n",
       "    <th>0.761328</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>962</th>\n",
       "    <th>14.482293</th>\n",
       "    <th>25.848917</th>\n",
       "    <th>25.848917</th>\n",
       "    <th>0.766970</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>963</th>\n",
       "    <th>14.611059</th>\n",
       "    <th>26.764471</th>\n",
       "    <th>26.764471</th>\n",
       "    <th>0.767996</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>964</th>\n",
       "    <th>14.485779</th>\n",
       "    <th>26.143929</th>\n",
       "    <th>26.143929</th>\n",
       "    <th>0.764239</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>965</th>\n",
       "    <th>14.441676</th>\n",
       "    <th>25.693165</th>\n",
       "    <th>25.693165</th>\n",
       "    <th>0.755056</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>966</th>\n",
       "    <th>14.467827</th>\n",
       "    <th>26.041565</th>\n",
       "    <th>26.041565</th>\n",
       "    <th>0.764865</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>967</th>\n",
       "    <th>14.534319</th>\n",
       "    <th>26.046585</th>\n",
       "    <th>26.046585</th>\n",
       "    <th>0.757850</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>968</th>\n",
       "    <th>14.517735</th>\n",
       "    <th>25.870264</th>\n",
       "    <th>25.870264</th>\n",
       "    <th>0.757338</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>969</th>\n",
       "    <th>14.537982</th>\n",
       "    <th>26.156582</th>\n",
       "    <th>26.156582</th>\n",
       "    <th>0.760686</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>970</th>\n",
       "    <th>14.492589</th>\n",
       "    <th>25.648756</th>\n",
       "    <th>25.648756</th>\n",
       "    <th>0.760966</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>971</th>\n",
       "    <th>14.621933</th>\n",
       "    <th>26.468296</th>\n",
       "    <th>26.468296</th>\n",
       "    <th>0.764698</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>972</th>\n",
       "    <th>14.517736</th>\n",
       "    <th>26.005163</th>\n",
       "    <th>26.005163</th>\n",
       "    <th>0.762534</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>973</th>\n",
       "    <th>14.522255</th>\n",
       "    <th>26.525499</th>\n",
       "    <th>26.525499</th>\n",
       "    <th>0.768614</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>974</th>\n",
       "    <th>14.453013</th>\n",
       "    <th>26.134624</th>\n",
       "    <th>26.134624</th>\n",
       "    <th>0.762355</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>975</th>\n",
       "    <th>14.566785</th>\n",
       "    <th>25.866787</th>\n",
       "    <th>25.866787</th>\n",
       "    <th>0.755086</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>976</th>\n",
       "    <th>14.551047</th>\n",
       "    <th>25.992912</th>\n",
       "    <th>25.992912</th>\n",
       "    <th>0.761108</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>977</th>\n",
       "    <th>14.490765</th>\n",
       "    <th>26.232296</th>\n",
       "    <th>26.232296</th>\n",
       "    <th>0.771637</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>978</th>\n",
       "    <th>14.522392</th>\n",
       "    <th>25.993315</th>\n",
       "    <th>25.993315</th>\n",
       "    <th>0.767810</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>979</th>\n",
       "    <th>14.518806</th>\n",
       "    <th>26.093327</th>\n",
       "    <th>26.093327</th>\n",
       "    <th>0.765765</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>980</th>\n",
       "    <th>14.488105</th>\n",
       "    <th>26.447437</th>\n",
       "    <th>26.447437</th>\n",
       "    <th>0.771390</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>981</th>\n",
       "    <th>14.555191</th>\n",
       "    <th>26.217859</th>\n",
       "    <th>26.217859</th>\n",
       "    <th>0.763901</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>982</th>\n",
       "    <th>14.487690</th>\n",
       "    <th>26.265284</th>\n",
       "    <th>26.265284</th>\n",
       "    <th>0.758498</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>983</th>\n",
       "    <th>14.426672</th>\n",
       "    <th>26.230455</th>\n",
       "    <th>26.230455</th>\n",
       "    <th>0.762212</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>984</th>\n",
       "    <th>14.523952</th>\n",
       "    <th>25.865162</th>\n",
       "    <th>25.865162</th>\n",
       "    <th>0.766713</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>985</th>\n",
       "    <th>14.580906</th>\n",
       "    <th>26.359619</th>\n",
       "    <th>26.359619</th>\n",
       "    <th>0.762557</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>986</th>\n",
       "    <th>14.475193</th>\n",
       "    <th>25.859867</th>\n",
       "    <th>25.859867</th>\n",
       "    <th>0.772180</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>987</th>\n",
       "    <th>14.509753</th>\n",
       "    <th>25.614220</th>\n",
       "    <th>25.614220</th>\n",
       "    <th>0.768907</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>988</th>\n",
       "    <th>14.518730</th>\n",
       "    <th>26.044237</th>\n",
       "    <th>26.044237</th>\n",
       "    <th>0.765682</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>989</th>\n",
       "    <th>14.495553</th>\n",
       "    <th>26.205326</th>\n",
       "    <th>26.205326</th>\n",
       "    <th>0.760629</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>990</th>\n",
       "    <th>14.469758</th>\n",
       "    <th>25.873768</th>\n",
       "    <th>25.873768</th>\n",
       "    <th>0.760248</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>991</th>\n",
       "    <th>14.458931</th>\n",
       "    <th>26.342457</th>\n",
       "    <th>26.342457</th>\n",
       "    <th>0.770224</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>992</th>\n",
       "    <th>14.479295</th>\n",
       "    <th>26.079416</th>\n",
       "    <th>26.079416</th>\n",
       "    <th>0.766711</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>993</th>\n",
       "    <th>14.449188</th>\n",
       "    <th>26.142344</th>\n",
       "    <th>26.142344</th>\n",
       "    <th>0.768283</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>994</th>\n",
       "    <th>14.530713</th>\n",
       "    <th>26.384750</th>\n",
       "    <th>26.384750</th>\n",
       "    <th>0.757795</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>995</th>\n",
       "    <th>14.406181</th>\n",
       "    <th>25.336462</th>\n",
       "    <th>25.336462</th>\n",
       "    <th>0.759648</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>996</th>\n",
       "    <th>14.553648</th>\n",
       "    <th>26.861990</th>\n",
       "    <th>26.861990</th>\n",
       "    <th>0.757820</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>997</th>\n",
       "    <th>14.374414</th>\n",
       "    <th>25.913578</th>\n",
       "    <th>25.913578</th>\n",
       "    <th>0.762935</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>998</th>\n",
       "    <th>14.445316</th>\n",
       "    <th>25.414080</th>\n",
       "    <th>25.414080</th>\n",
       "    <th>0.771487</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>999</th>\n",
       "    <th>14.475859</th>\n",
       "    <th>25.953630</th>\n",
       "    <th>25.953630</th>\n",
       "    <th>0.761543</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>1000</th>\n",
       "    <th>14.422099</th>\n",
       "    <th>26.436419</th>\n",
       "    <th>26.436419</th>\n",
       "    <th>0.761298</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l.fit_one_cycle(int(environ['n_epochs']), float(environ['lr']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2880x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "l.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "l.save(f\"r_speedup_{optimizer}_batch_norm_{batch_norm}_{loss_func}_nlayers_{len(layers_sizes)}_log_{log}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "old_models\r\n",
      "old_repr\r\n",
      "r_speedup_Adam_batch_norm_True_MAPE_nlayers_5_log_False.pth\r\n",
      "speedup_Adam_batch_norm_True_MAPE_nlayers_5_log_False2.pth\r\n",
      "speedup_Adam_batch_norm_True_MAPE_nlayers_5_log_False.pth\r\n",
      "speedup_Adam_batch_norm_True_MSE_nlayers_5_log_False.pth\r\n",
      "speedup_Adam_batch_norm_True_MSE_nlayers_5_log_True.pth\r\n",
      "tmp.pth\r\n"
     ]
    }
   ],
   "source": [
    "!ls models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_df = get_results_df(val_dl, l.model)\n",
    "train_df = get_results_df(train_dl, l.model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = train_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>245283.000000</td>\n",
       "      <td>245283.000000</td>\n",
       "      <td>245283.000000</td>\n",
       "      <td>245283.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.037621</td>\n",
       "      <td>1.135724</td>\n",
       "      <td>0.152116</td>\n",
       "      <td>12.830106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.231515</td>\n",
       "      <td>1.405682</td>\n",
       "      <td>0.464680</td>\n",
       "      <td>28.543703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010180</td>\n",
       "      <td>0.008491</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.264881</td>\n",
       "      <td>0.278690</td>\n",
       "      <td>0.002987</td>\n",
       "      <td>0.895514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.844768</td>\n",
       "      <td>0.899071</td>\n",
       "      <td>0.023104</td>\n",
       "      <td>5.397871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.998950</td>\n",
       "      <td>1.036481</td>\n",
       "      <td>0.100247</td>\n",
       "      <td>13.001473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.480000</td>\n",
       "      <td>16.089287</td>\n",
       "      <td>15.559452</td>\n",
       "      <td>848.957703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          prediction         target       abs_diff            APE\n",
       "count  245283.000000  245283.000000  245283.000000  245283.000000\n",
       "mean        1.037621       1.135724       0.152116      12.830106\n",
       "std         1.231515       1.405682       0.464680      28.543703\n",
       "min         0.010180       0.008491       0.000000       0.000000\n",
       "25%         0.264881       0.278690       0.002987       0.895514\n",
       "50%         0.844768       0.899071       0.023104       5.397871\n",
       "75%         0.998950       1.036481       0.100247      13.001473\n",
       "max         9.480000      16.089287      15.559452     848.957703"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = val_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.121260</td>\n",
       "      <td>1.430897</td>\n",
       "      <td>0.390623</td>\n",
       "      <td>27.763691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.156423</td>\n",
       "      <td>1.683147</td>\n",
       "      <td>0.679037</td>\n",
       "      <td>41.883354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.018547</td>\n",
       "      <td>0.014795</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.007319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.377303</td>\n",
       "      <td>0.395193</td>\n",
       "      <td>0.011900</td>\n",
       "      <td>3.115584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.993079</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.113269</td>\n",
       "      <td>20.872551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.165657</td>\n",
       "      <td>1.621683</td>\n",
       "      <td>0.391169</td>\n",
       "      <td>43.921596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.675441</td>\n",
       "      <td>10.872228</td>\n",
       "      <td>4.920828</td>\n",
       "      <td>844.224915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         prediction        target      abs_diff           APE\n",
       "count  10000.000000  10000.000000  10000.000000  10000.000000\n",
       "mean       1.121260      1.430897      0.390623     27.763691\n",
       "std        1.156423      1.683147      0.679037     41.883354\n",
       "min        0.018547      0.014795      0.000046      0.007319\n",
       "25%        0.377303      0.395193      0.011900      3.115584\n",
       "50%        0.993079      1.000000      0.113269     20.872551\n",
       "75%        1.165657      1.621683      0.391169     43.921596\n",
       "max        7.675441     10.872228      4.920828    844.224915"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2074.000000</td>\n",
       "      <td>2074.0</td>\n",
       "      <td>2074.000000</td>\n",
       "      <td>2074.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.985529</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.017589</td>\n",
       "      <td>1.758861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.111946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.111498</td>\n",
       "      <td>11.149847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.071318</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.007319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.997701</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001470</td>\n",
       "      <td>0.147021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.998260</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001813</td>\n",
       "      <td>0.181329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.998590</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.002428</td>\n",
       "      <td>0.242829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.891778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.928682</td>\n",
       "      <td>92.868195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prediction  target     abs_diff          APE\n",
       "count  2074.000000  2074.0  2074.000000  2074.000000\n",
       "mean      0.985529     1.0     0.017589     1.758861\n",
       "std       0.111946     0.0     0.111498    11.149847\n",
       "min       0.071318     1.0     0.000073     0.007319\n",
       "25%       0.997701     1.0     0.001470     0.147021\n",
       "50%       0.998260     1.0     0.001813     0.181329\n",
       "75%       0.998590     1.0     0.002428     0.242829\n",
       "max       1.891778     1.0     0.928682    92.868195"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange==0) & (df.unroll == 0) & (df.tile == 0)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>725.000000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>725.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.301574</td>\n",
       "      <td>1.519225</td>\n",
       "      <td>0.354056</td>\n",
       "      <td>23.900990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.112958</td>\n",
       "      <td>1.422773</td>\n",
       "      <td>0.483243</td>\n",
       "      <td>19.511810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.158533</td>\n",
       "      <td>0.091200</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.020753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.432121</td>\n",
       "      <td>0.508859</td>\n",
       "      <td>0.065178</td>\n",
       "      <td>8.419112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.975921</td>\n",
       "      <td>0.993141</td>\n",
       "      <td>0.172049</td>\n",
       "      <td>18.377670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.975362</td>\n",
       "      <td>2.207209</td>\n",
       "      <td>0.414999</td>\n",
       "      <td>34.993168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.673403</td>\n",
       "      <td>9.358214</td>\n",
       "      <td>3.923676</td>\n",
       "      <td>126.306580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       prediction      target    abs_diff         APE\n",
       "count  725.000000  725.000000  725.000000  725.000000\n",
       "mean     1.301574    1.519225    0.354056   23.900990\n",
       "std      1.112958    1.422773    0.483243   19.511810\n",
       "min      0.158533    0.091200    0.000204    0.020753\n",
       "25%      0.432121    0.508859    0.065178    8.419112\n",
       "50%      0.975921    0.993141    0.172049   18.377670\n",
       "75%      1.975362    2.207209    0.414999   34.993168\n",
       "max      7.673403    9.358214    3.923676  126.306580"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange==0) & (df.unroll == 0) & (df.tile == 1)][['index','name','prediction','target', 'abs_diff','APE']].to_csv(path_or_buf='./eval_results.csv',sep=';')\n",
    "df[(df.interchange==0) & (df.unroll == 0) & (df.tile == 1)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>281.000000</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>281.000000</td>\n",
       "      <td>281.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.309521</td>\n",
       "      <td>6.044158</td>\n",
       "      <td>1.815515</td>\n",
       "      <td>61.342331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.116049</td>\n",
       "      <td>3.221366</td>\n",
       "      <td>1.237432</td>\n",
       "      <td>163.233612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.979377</td>\n",
       "      <td>0.105179</td>\n",
       "      <td>0.000852</td>\n",
       "      <td>0.086115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.074235</td>\n",
       "      <td>2.546717</td>\n",
       "      <td>0.614721</td>\n",
       "      <td>16.486435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.464447</td>\n",
       "      <td>7.389543</td>\n",
       "      <td>1.802265</td>\n",
       "      <td>28.025612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.544522</td>\n",
       "      <td>8.668690</td>\n",
       "      <td>2.898372</td>\n",
       "      <td>38.227890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.327499</td>\n",
       "      <td>10.872228</td>\n",
       "      <td>4.777884</td>\n",
       "      <td>844.224915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       prediction      target    abs_diff         APE\n",
       "count  281.000000  281.000000  281.000000  281.000000\n",
       "mean     4.309521    6.044158    1.815515   61.342331\n",
       "std      2.116049    3.221366    1.237432  163.233612\n",
       "min      0.979377    0.105179    0.000852    0.086115\n",
       "25%      2.074235    2.546717    0.614721   16.486435\n",
       "50%      5.464447    7.389543    1.802265   28.025612\n",
       "75%      5.544522    8.668690    2.898372   38.227890\n",
       "max      7.327499   10.872228    4.777884  844.224915"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange==0) & (df.unroll == 1) & (df.tile == 0)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>232.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>232.000000</td>\n",
       "      <td>232.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.756619</td>\n",
       "      <td>0.918692</td>\n",
       "      <td>0.222288</td>\n",
       "      <td>21.729357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.914184</td>\n",
       "      <td>1.168210</td>\n",
       "      <td>0.461534</td>\n",
       "      <td>20.075941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.020958</td>\n",
       "      <td>0.018092</td>\n",
       "      <td>0.000089</td>\n",
       "      <td>0.079193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.238594</td>\n",
       "      <td>0.265846</td>\n",
       "      <td>0.022004</td>\n",
       "      <td>6.248699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.396349</td>\n",
       "      <td>0.466655</td>\n",
       "      <td>0.070740</td>\n",
       "      <td>16.291368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.876001</td>\n",
       "      <td>0.956079</td>\n",
       "      <td>0.203351</td>\n",
       "      <td>28.746875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.947685</td>\n",
       "      <td>8.069739</td>\n",
       "      <td>3.122054</td>\n",
       "      <td>112.560829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       prediction      target    abs_diff         APE\n",
       "count  232.000000  232.000000  232.000000  232.000000\n",
       "mean     0.756619    0.918692    0.222288   21.729357\n",
       "std      0.914184    1.168210    0.461534   20.075941\n",
       "min      0.020958    0.018092    0.000089    0.079193\n",
       "25%      0.238594    0.265846    0.022004    6.248699\n",
       "50%      0.396349    0.466655    0.070740   16.291368\n",
       "75%      0.876001    0.956079    0.203351   28.746875\n",
       "max      4.947685    8.069739    3.122054  112.560829"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange==1) & (df.unroll == 0) & (df.tile == 0)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>868.000000</td>\n",
       "      <td>868.000000</td>\n",
       "      <td>868.000000</td>\n",
       "      <td>868.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.373762</td>\n",
       "      <td>1.748770</td>\n",
       "      <td>0.477148</td>\n",
       "      <td>31.763561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.171251</td>\n",
       "      <td>1.605983</td>\n",
       "      <td>0.570058</td>\n",
       "      <td>44.691597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.164368</td>\n",
       "      <td>0.057130</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.045584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.428907</td>\n",
       "      <td>0.695116</td>\n",
       "      <td>0.085793</td>\n",
       "      <td>9.017995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.032962</td>\n",
       "      <td>1.241629</td>\n",
       "      <td>0.282237</td>\n",
       "      <td>26.219162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.054201</td>\n",
       "      <td>2.229650</td>\n",
       "      <td>0.713841</td>\n",
       "      <td>44.385671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.675441</td>\n",
       "      <td>10.137201</td>\n",
       "      <td>4.077254</td>\n",
       "      <td>401.347290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       prediction      target    abs_diff         APE\n",
       "count  868.000000  868.000000  868.000000  868.000000\n",
       "mean     1.373762    1.748770    0.477148   31.763561\n",
       "std      1.171251    1.605983    0.570058   44.691597\n",
       "min      0.164368    0.057130    0.000118    0.045584\n",
       "25%      0.428907    0.695116    0.085793    9.017995\n",
       "50%      1.032962    1.241629    0.282237   26.219162\n",
       "75%      2.054201    2.229650    0.713841   44.385671\n",
       "max      7.675441   10.137201    4.077254  401.347290"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange==0) & (df.unroll == 1) & (df.tile == 1)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1276.000000</td>\n",
       "      <td>1276.000000</td>\n",
       "      <td>1276.000000</td>\n",
       "      <td>1276.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.012736</td>\n",
       "      <td>2.878316</td>\n",
       "      <td>0.996945</td>\n",
       "      <td>31.574406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.366116</td>\n",
       "      <td>2.108479</td>\n",
       "      <td>1.017374</td>\n",
       "      <td>21.246101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.033753</td>\n",
       "      <td>0.042484</td>\n",
       "      <td>0.000265</td>\n",
       "      <td>0.022992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.717553</td>\n",
       "      <td>0.977715</td>\n",
       "      <td>0.163885</td>\n",
       "      <td>11.196567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.116860</td>\n",
       "      <td>2.629361</td>\n",
       "      <td>0.559276</td>\n",
       "      <td>29.473694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.658386</td>\n",
       "      <td>4.365878</td>\n",
       "      <td>1.821425</td>\n",
       "      <td>49.913550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.868882</td>\n",
       "      <td>9.784180</td>\n",
       "      <td>4.920828</td>\n",
       "      <td>132.563797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prediction       target     abs_diff          APE\n",
       "count  1276.000000  1276.000000  1276.000000  1276.000000\n",
       "mean      2.012736     2.878316     0.996945    31.574406\n",
       "std       1.366116     2.108479     1.017374    21.246101\n",
       "min       0.033753     0.042484     0.000265     0.022992\n",
       "25%       0.717553     0.977715     0.163885    11.196567\n",
       "50%       2.116860     2.629361     0.559276    29.473694\n",
       "75%       2.658386     4.365878     1.821425    49.913550\n",
       "max       4.868882     9.784180     4.920828   132.563797"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange==1) & (df.unroll == 1) & (df.tile == 0)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1663.000000</td>\n",
       "      <td>1663.000000</td>\n",
       "      <td>1663.000000</td>\n",
       "      <td>1663.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.759392</td>\n",
       "      <td>0.937209</td>\n",
       "      <td>0.260730</td>\n",
       "      <td>28.067686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.769510</td>\n",
       "      <td>1.023356</td>\n",
       "      <td>0.399831</td>\n",
       "      <td>20.749691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.018548</td>\n",
       "      <td>0.014836</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.061909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.210683</td>\n",
       "      <td>0.246893</td>\n",
       "      <td>0.039022</td>\n",
       "      <td>10.700531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.494625</td>\n",
       "      <td>0.624512</td>\n",
       "      <td>0.122702</td>\n",
       "      <td>24.871428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.030154</td>\n",
       "      <td>1.173670</td>\n",
       "      <td>0.315577</td>\n",
       "      <td>43.254141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.934253</td>\n",
       "      <td>8.724252</td>\n",
       "      <td>3.983392</td>\n",
       "      <td>139.318390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prediction       target     abs_diff          APE\n",
       "count  1663.000000  1663.000000  1663.000000  1663.000000\n",
       "mean      0.759392     0.937209     0.260730    28.067686\n",
       "std       0.769510     1.023356     0.399831    20.749691\n",
       "min       0.018548     0.014836     0.000046     0.061909\n",
       "25%       0.210683     0.246893     0.039022    10.700531\n",
       "50%       0.494625     0.624512     0.122702    24.871428\n",
       "75%       1.030154     1.173670     0.315577    43.254141\n",
       "max       4.934253     8.724252     3.983392   139.318390"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange==1) & (df.unroll == 0) & (df.tile == 1)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2881.000000</td>\n",
       "      <td>2881.000000</td>\n",
       "      <td>2881.000000</td>\n",
       "      <td>2881.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.629967</td>\n",
       "      <td>0.858291</td>\n",
       "      <td>0.323311</td>\n",
       "      <td>41.598583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.725950</td>\n",
       "      <td>1.094979</td>\n",
       "      <td>0.488463</td>\n",
       "      <td>37.766975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.018547</td>\n",
       "      <td>0.014795</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.023296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.158124</td>\n",
       "      <td>0.183774</td>\n",
       "      <td>0.055104</td>\n",
       "      <td>19.515968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.401537</td>\n",
       "      <td>0.424563</td>\n",
       "      <td>0.149758</td>\n",
       "      <td>38.063084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.849428</td>\n",
       "      <td>1.033311</td>\n",
       "      <td>0.366112</td>\n",
       "      <td>53.976719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.855897</td>\n",
       "      <td>9.207530</td>\n",
       "      <td>4.390360</td>\n",
       "      <td>447.704224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prediction       target     abs_diff          APE\n",
       "count  2881.000000  2881.000000  2881.000000  2881.000000\n",
       "mean      0.629967     0.858291     0.323311    41.598583\n",
       "std       0.725950     1.094979     0.488463    37.766975\n",
       "min       0.018547     0.014795     0.000056     0.023296\n",
       "25%       0.158124     0.183774     0.055104    19.515968\n",
       "50%       0.401537     0.424563     0.149758    38.063084\n",
       "75%       0.849428     1.033311     0.366112    53.976719\n",
       "max       4.855897     9.207530     4.390360   447.704224"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange==1) & (df.unroll == 1) & (df.tile == 1)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "      <th>abs_diff</th>\n",
       "      <th>APE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7926.000000</td>\n",
       "      <td>7926.000000</td>\n",
       "      <td>7926.000000</td>\n",
       "      <td>7926.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.156775</td>\n",
       "      <td>1.543651</td>\n",
       "      <td>0.488235</td>\n",
       "      <td>34.568348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.295356</td>\n",
       "      <td>1.874331</td>\n",
       "      <td>0.729769</td>\n",
       "      <td>44.243641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.018547</td>\n",
       "      <td>0.014795</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.020753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.289361</td>\n",
       "      <td>0.312547</td>\n",
       "      <td>0.060371</td>\n",
       "      <td>12.950603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.676460</td>\n",
       "      <td>0.830516</td>\n",
       "      <td>0.194361</td>\n",
       "      <td>28.857708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.662223</td>\n",
       "      <td>2.132191</td>\n",
       "      <td>0.514185</td>\n",
       "      <td>48.964257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.675441</td>\n",
       "      <td>10.872228</td>\n",
       "      <td>4.920828</td>\n",
       "      <td>844.224915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prediction       target     abs_diff          APE\n",
       "count  7926.000000  7926.000000  7926.000000  7926.000000\n",
       "mean      1.156775     1.543651     0.488235    34.568348\n",
       "std       1.295356     1.874331     0.729769    44.243641\n",
       "min       0.018547     0.014795     0.000046     0.020753\n",
       "25%       0.289361     0.312547     0.060371    12.950603\n",
       "50%       0.676460     0.830516     0.194361    28.857708\n",
       "75%       1.662223     2.132191     0.514185    48.964257\n",
       "max       7.675441    10.872228     4.920828   844.224915"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.interchange + df.tile + df.unroll != 0)][['prediction','target', 'abs_diff','APE']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JSUQyWlZWFpFIhEgkEnQoUo2Sk4hkpM2bNwPwve99j9dee41u3boFHJFUF8gy7WZ2nZm9Z2aLzOwFM+ud7jhERCq9+eabDBgwoGp2cTMLOCKpLahl2qe6+xB3HwrMIs61nUREUmHw4MEce+yxHHTQQUGHIvUIZJl2d99S7WUHtFS7iDSDZcuWUV5eTteuXXnooYfo0aNH42+SQAR2zcnMbjCzNcAkGmg5acliEUmFdevWMWLECK688sqgQ2mQznlRgSUnd7/S3fcCHgEubOA4LVksIknr3bs3U6ZM4cIL6z3dhILOeVFhGK03HTgh6CBEpHUqLCxk+fLlAJx33nn07ds34IgkHoHMEGFm+7r7x7GX44EPg4hDRFq38vJyTj31VPLz83n11Vc1Kq8FSXtyii3TPhrobmZrgauBY8xsMFABrAJ+lu44RCTzRCIRnnzySTp06KDE1MKkPTm5+2l1bL6vjm0iIinx/vvvs2DBAv77v/+bAw88MOhwJAFhuOYkIpJSt956K7/5zW/YsmVL4wdLKGlWchFpdf70pz+xbt06OnfuHHQokiC1nESkVVi+fDmTJk1i27ZttG3blgEDBgQdkiRByUlEWoVFixYxb9481qxZE3QokgJKTiLSolVUVABw4okn8vHHH7PffvsFHJGkgpKTiLRYq1evZvjw4bzxxhsAdOrUKeCIJFWUnESkxcrKyqJNmzZkZ2cHHYqkmEbriUiLs3nzZjp37kzfvn156623dINtK6SWk4i0KJs2bWLkyJH89rfRxQyUmFonJScRaVHy8vL44Q9/yNixY4MORdKoOebWux84Fljv7gfEtk0FfgjsBD4Bznb3knTHIiItV3FxMe5Ojx49mDp1atDhSJoFtUz7XOAAdx8CfARc0QxxiEgL5e5MmDCBY445pmrouLRuzTHx6ytmNqDWtheqvXwTODHdcYhIy2Vm3HTTTezcuZOsLF2NyARh+Cn/NzC7vp1aslgkc5WUlDB37lwAvvOd7/C9730v4IjST+e8qECTk5ldCewiulR7nbRksUjm+vWvf81xxx1HJp2kdc6LCiw5mdmPiQ6UmOTuHlQcIhJeU6ZMYdasWWTySTpTBZKczGwccBkw3t23BxGDiITT1q1bufbaaykrK6Nz586MHj066JAkAGlPTrFl2t8ABpvZWjP7CXAX0AmYa2aLzOxP6Y5DRFqGWbNmce2117JgwYKgQ5EAaZl2EQmV0047jeHDh/ONb3wj6FAkQGEYrSciGa60tJQzzjiDDz/8EECJSZScRCR4RUVFzJs3j8LCwqBDkZDQrOQiEpiKigqysrIYNGgQH330ER07dgw6JAkJtZxEJBA7d+7kuOOO4/bbbwdQYpIalJxEJBBmRtu2bWnfvn3QoUgIqVtPRJpVWVkZO3bsoGPHjjz++ONaj0nqpJaTiDSrs88+m3HjxlFWVqbEJPVSy0lEmtXEiRNZvXo12dnZQYciIabkJCJpV15ezkcffcT+++/PiSdqhRxpnLr1RCTtfve73/Ff//VfrFq1KuhQpIUIapn2k4BrgP2BEe6+MN1xiEhwzj//fHr27En//v2DDkVaiKCWaV8KHA+80gz1i0gA3J0nnngCd6dXr15ccMEFQYckLUjak5O7vwJsrLXtA3dflu66RSQ4M2bM4OSTT+aZZ54JOhRpgUJ/zUlLFou0TMcddxyzZs3ihz/8YdChtCg650WFPjlpyWKRlsPdueWWW1i3bh1mxg9+8APdy9REOudFhT45iUjLsXLlSn73u99x//33Bx2KtHC6z0lEUmbvvfemsLCQffbZJ+hQpIULZJl2MzvOzNYChwLPmtmcdMchIulz3XXX8be//Q2AQYMGqStPkhbUMu0AM9Jdt4ik386dO/nnP//J6tWrOfXUU4MOR1oJdeuJSMIqKipo27Yts2fPpl27dkGHI62IBkSISEJuueUWjj/+eHbu3Elubi6RSCTokKQVUXISkYS0b9+enJwcsrJ0GpHU02+ViDRJSUkJABdeeCHTp0+nTRtdHZDUU3ISkbg98MADDB48mOXLlwNoVJ6kjZKTiMRt1KhRTJgwgX79+gUdirRySk4i0qilS5cCsO+++3LPPffQtm3bgCOS1k7JSUQaNH/+fIYMGVJ1k61Ic1ByEpEGHX744dx8881MmDAh6FAkgyg5iUid5syZw+bNm2nTpg2XXnopOTk5QYckGaQ55ta738zWm9nSatu6mdlcM/s49rVruuMQkfh98cUXHHfccVx11VVBhyIZKqhl2i8H5rn7vsC82GsRCYmePXvy3HPPcdNNNwUdimSoQJZpByYAD8aePwhMTHccItK4f/zjH8ybNw+A0aNH07Fjx4AjkkwV1K3dPd39MwB3/8zMetR3oJlNBiYDurdCJI0qKiq49tpr6dixI0ceeaRusA2IznlRoZ93xN3vAe4BKCgo8IDDEWm1srKyeP7552nTpo0SU4B0zosKarTeF2a2J0Ds6/qA4hDJeHPnzuXiiy+moqKC7t27k5eXF3RIIoElp38AP449/zHw94DiEMl4r732GvPnz2fr1q1BhyJSJZBl2oEpwPfM7GPge7HXItKMKioqALjmmmt4/fXX6dKlS8ARifxHc4zWO83d93T3bHfv6+73ufuX7n6Uu+8b+1p7NJ+IpNFrr73GsGHDWL16NWZGhw4dgg5JpAbNECGSgdq2bUtubq4mcJXQCv1oPRFJnZKSEvLy8hgxYgSvv/66RuVJaKnlJJIhli5dysCBA3niiScALRQo4abkJJIhBg4cyHHHHcfIkSODDkWkUerWE2nlPvroI/r3709ubi733Xdf0OGIxEUtJ5FWrKSkhMMPP5zzzz8/6FBEmkQtJ5FWLC8vj1tvvZVDDz006FBEmkTJSaQV+vDDD/n6668ZOnQoZ5xxRtDhiDSZkpNIK+PunH322WzevJklS5YQiUSCDkmkyZScRFoZM2P69OmUlpYqMUmLFeiACDO72MyWmtn7ZnZJkLGItHQrVqxg2rRpAOy9995885vfDDgikcQFlpzM7ADgHGAEcBBwrJntG1Q8Ii3d//3f/3Hdddfx+eefBx2KSNKCbDntD7zp7tvdfRfwMnBcgPGItGg33ngjb7/9Nr169Qo6FJGkBZmclgJHmNkeZpYLHAPsVfsgM5tsZgvNbGFxcXGzBykSZmvXruWkk05i48aNRCIRBg4cGHRIkiSd86ICS07u/gFwMzAXeB5YDOyq47h73L3A3Qvy8/ObOUqRcFu2bBmvvPIKK1euDDoUSRGd86ICHa3n7vcB9wGY2Y3A2iDjEWkpKioqyMrK4qijjmLFihVaj0lanaBH6/WIfe0HHA88GmQ8Ii3BF198QUFBAbNnzwZQYpJWKej7nJ4ysz2AMuACd98UcDwioZednU3Hjh3Jzc0NOhSRtAm6W+/bQdYv0pJs3ryZTp060a1bN15++WWtxyStmmYlF2kBSktLGT16dNXs4kpM0toF3a0nInHIycnhxBNPZPjw4UGHItIslJxEQmzz5s2UlJTQv39/rrzyyqDDEWk26tYTCbFJkyZx1FFHsXPnzqBDEWlWajmJhNj111/PqlWraNu2bdChiDQrtZxEQmbbtm384x//AGDo0KFMmDAh4IhEmp+Sk0jITJkyhRNOOIEVK1YEHYpIYNStJxIyV155JaNHj9YkrpLR1HISCYGvv/6aq6++mu3bt9O+fXuOOuqooEMSCZSSk0gIvPLKK9xwww289NJLQYciEgqBduuZ2c+BnwIOLAHOdvevg4xJJAhHH300y5YtY5999gk6FJFQCHKZ9j7ARUCBux8ARIBTg4pHpLmVlZVx1lln8dZbbwEoMYlUE3S3Xhsgx8zaALnAuoDjEWk2GzZs4LXXXuPdd98NOhSR0AmsW8/di8zsFmA1UAq84O4v1D7OzCYDkwH69evXvEGKpEFFRQVmxp577snixYu1HpPUoHNeVJDdel2BCcDeQG+gg5n9qPZxWrJYWpPy8nLOOOOMqnnylJikNp3zooLs1hsDfOruxe5eBjwNHBZgPCJpZ2Z06tSJzp07Bx2KSKgFOVpvNXCImeUS7dY7ClgYYDwiaVNRUcHWrVvp0qULd999d9V6TDMLi5g6ZxnrSkrpnZfDpWMHM3FYn2aLK+j6ReoT5DWnBWb2JPAusAsoBO4JKh6RdLr44ot5+eWXeeONN6q68mYWFnHF00soLSsHoKiklCueXgKQVIKIN+Gkq36RVAh6mfargauDjEGkOUycOJH8/Hxyc3Ortk2ds6wqMVQqLStn6pxluyWHyoRTVFJKxIxyd/rUkXiaknCaUr9Ic4srOZlZO+AEYED197j7tekJS6Tlc3eWLFnCkCFDOOqoo3abkmhdSWmd76u9vXbCKXcH6k48TUk4DdWv7r5w2Lgtc9fxindAxN+JjqzbBWyr9hCRetxxxx0UFBSwZMmSOvf3zsuJa3tdCadSZeKpFG/Ca6j+LjnZXPH0EopKSnH+kwRnFhbVebxIOsTbrdfX3celNRKRVuass84iKyuLAw44oM79l44dXKNFBJCTHeHSsYNrHFdfwqlrf++8HIriTET11W+GuvskcPG2nF43swPTGolIK+DuPP744+zatYu8vDwuuuiiqpF5tU0c1oebjj+QPnk5GNAnL4ebjj9wtwRQXwunrv2Xjh1MTnakxv66El5D9ZdsL6uznsaSpEgqxdtyOhw4y8w+BXYABri7D0lbZCIt0Kuvvsopp5zC/fffz9lnn93o8ROH9Wm0NVJXC6dS7cRT/dpTPNeL6qq/cuBFbY0lSZFUijc5fT+tUYi0EkcccQSzZ8/m6KOPTlmZ1RNOY6P1Ko9Ppvst3u5GkXSKKzm5+yozOwj4dmzTq+6+OH1hibQsd9xxB8cccwz77rsv48al/vJssgmnqXVB/K0vSZ9uHdoGHUJg4h1KfjFwDtEphgAeNrN73P1/0xaZSAtRXFzMDTfcwOrVq7n11luDDiclmjMZitQl3m69nwAj3X0bgJndDLwBKDlJxsvPz+ftt9+mb9++QYci0mrEO1rPgOpXY8tj20Qy1rRp07jrrrsA6N+/P5FIpJF3iEi84k1OfwEWmNk1ZnYN8CZwXzIVm9lgM1tU7bHFzC5JpkyR5lJRUcHLL7/M/Pnz8diMDSKSOvEOiLjNzOYTHVJuwNnuXphMxe6+DBgKYGYRoAiYkUyZIs2hvLycSCTCY489hpnVex+TiCSuwZaTmXWOfe0GrAQeBh4CVsW2pcpRwCfuviqFZYqk3L333suYMWP46quvaNu2LdnZ2UGHJNIqNdZymg4cC7wDVO+7sNjrgSmK41Tg0bp2aMliCZNOnTrRqVMnJSVJG53zoizo/nIzawusA77l7l80dGxBQYEvXKj1CKX5bdy4kW7dop0F7q6uPElWXL9ArfScF9dnj2tAhJnNi2dbgr4PvNtYYhIJyowZMxg4cCCFhdHLrEpMIunXYLeembUHcoHuZtaV/2S8zkDvFMVwGvV06YmEwYgRIzjppJMYPFjT94g0l8ZaTucSvd60X+xr5ePvwB+SrdzMcoHv8Z+ZJ0RCY/Hixbg7ffr04d57762xiq2IpFeDycnd73D3vYFfuftAd9879jjI3e9KtnJ33+7ue7j75mTLEkmlRYsWMXz4cP74xz8GHYpIRor3JtwKM8urfGFmXc3s/DTFJBK4gw46iNtuu40zzjgj6FBEMlK8yekcdy+pfOHum4hOBCvSqsybN4/PP/8cM+Oiiy6ic+fOQYckkpHiTU5ZVm2IUmxGh8ydy11apa1bt3LyySfzi1/8IuhQRDJevLOSzwEeN7M/Eb359mfA82mLSiQAnTp1Yvbs2QwaNCjoUEQyXrzJ6TKiI/fOIzqc/AXgz+kKSqQ5vfTSS2zcuJETTjiBESNGBB2OiBD/xK8VwN2xh0ir4e5MmTKF9evXM2HCBNq0iff/NRFJp8Zuwn3c3U82syXUnFsPAHcfkrbIRJqBmfHkk09SWlqqxCQSIo39NV4c+3psugMRaU6vv/469957L/fcc0/VZK4iEh4NJid3/yz2VUtZSKvy7rvv8q9//YtNmzbRo0ePoMMRkVoaW89pa2yF2jofyVZuZnm1FQzYAAAgAElEQVRm9qSZfWhmH5jZocmWKdKQ8vJyAC688EIWLVqkxCQSUo1NX9TJ3TsD04DLgT5AX6Kj965PQf13AM+7+37AQcAHKShTpE6FhYUceOCB/Pvf/wbQXHkiIRbvFeCx7j6y2uu7zWwB8PtEK46tsnsEcBaAu+8EdiZankhjcnJy6NKli5KSSAsQ7wwR5WY2ycwiZpZlZpOA8iTrHggUA38xs0Iz+7OZdUiyTJHdbNy4EYD99tuP119/nQEDBgQbkIg0Kt7kdDpwMvBF7HFSbFsy2gAHA3e7+zBgG9GuwxrMbLKZLTSzhcXFxUlWKZnm008/Zf/99+fuu6O36GmhQAk7nfOi4kpO7r7S3Se4e3d3z3f3ie6+Msm61wJr3X1B7PWTRJNV7brvcfcCdy/Iz89PskrJNH379uWkk07iqKOOCjoUkbjonBcV7zLt3zCzeWa2NPZ6iJldlUzF7v45sMbMKpcXPQr4dzJlilT65JNP2LJlC9nZ2dx111184xvfCDokEWmCeLv17gWuAMoA3P094NQU1P8/wCNm9h4wFLgxBWVKhtuxYwdjxozRWkwiLVi8o/Vy3f2tWv31u5Kt3N0XAQXJliNSXbt27Zg2bRoDBw4MOhQRSVC8yWmDme1DbH49MzsR+CxtUYkkYOXKlaxdu5bDDz+cCRMmBB1Os5pZWMTUOctYV1JK77wcLh07mInD+gQdliRp47bMvbsm3uR0AXAPsJ+ZFQGfApPSFpVIAipnfVi+fDnt27cPOpy0uGrmEh5dsIZydyJmnDZyLwr6d+OKp5dQWha9u6OopJQrnl4CoAQlLVajycnMsoACdx8Tuw8py923pj80kaa5//77KSoqatWJ6eE3V1e9Lnfn4TdXM+PdoqrEVKm0rJypc5YpOUmL1eiAiNhaThfGnm9TYpIwWbduHTfccAPuTo8ePRg2bFggccwsLGLUlBfZ+/JnGTXlRWYWFqW8jkcXrKlz+7addd8Pv66kNOUxiDSXeEfrzTWzX5nZXmbWrfKR1shE4jB9+nSmTJnCJ598ElgMMwuLuOLpJRSVlOL8p1utsQTV1IRW7rstqdagLLO0JEmR5mAexy+8mX1K3YsNNutwqIKCAl+4cGFzVikh5+6sXLmSvffeO+111TfoYNSUFymqo5USMaPCnd55OXx3v3xe+rC46r3f3S+fp96p2R1nwKRD+nH9xAPrrH+fK55rcoLKzjI6tm9DyfYyDZQIl7imKhm4/xBf8cF76Y6lucX12eNNTjnA+cDhRJPUq8Cf3L1Z+w2UnASguLiYyZMn87//+7/07du3WeqcWVjEpU8upqy85t9LlkFF0/IFEP3rrOttBtx+ytA6E0jta06JyMmOcNPxBypBBS/u5HT9A7MAOH1kv7QG1Izi+uzxdus9COwP3An8b+z5g4nFJZKcVatW8cYbb7BixYpmq/N3z7y/W2KCxBIT1J2YKrdPnbOszn3XTzyQHx3Sj0gS8wNWDpQQCbt4h5IPdveDqr1+ycwWpyMgkfqUl5cTiUQoKChgxYoVzbr0xabtZc1WV0MDGa6feCDXTzyQmYVF/PyxRfUmuUTLFwmLeFtOhWZ2SOULMxsJ/Cs9IYnsrqSkhFGjRjF9+nSgdS8U2Dsvp9FjJg7rw6RD+sXXP5JA+SJBizc5jQReN7OVZrYSeAP4jpktic2Ll5BYeUvMbJGZ6WKS1Cs7O5uuXbvSpUuXQOrPy8mO67iIGV1zGz+2vqRiwKVjB9ezt6brJx7I7acMpU9eDgb0ycvhR4f0q3qdl5NNdqRmTTnZkbjLFwlSvN1649IYw3fdfUMay5cWbOvWrbRr144OHTrw3HPPJb0eU10zLNQ3Oq66Yw/aM67BCO2zs/jBkD157O01Na5RRbKMTu3asLm0rNHRek0ZrDBxWJ8Gj9e0RtJSxZWc3H1VugMRqW3Xrl0cc8wx5Ofn89RTT6UkMdU1wwLQYIK6auYSHqmVmAwY1KMDy9dvq3HdZ9vOcp56p4hT/muvGkPHayeFmYVFzFr8WVVy6pqbzdU//FbKE0djyUvCrxWN0muSeFtO6eLAC2bmwP+5+z21DzCzycBkgH79MvOHlKnatGnD6aefTn5+fkpWsK1vhoVHF6ypNznNLCzikTdX7zbwwIHtOyvonZez2z1OpWXlvPRhMf+6/Mh6y6w+Fx7A12UVcX8Oad2qn/O698rcfyziveaULqPc/WDg+8AFZnZE7QO0KmTm2b59O8uWRYc7n3feeZx44okpKbe+G1gburF16pxl9Y6IW1dSWu/It4ZGxE2ds6zeufBEqp/zOuVl7kQ8gSYnd18X+7oemAGMCDIeCYfzzjuPI444gi1btqS03PruD2rovqGGkkzvvJx6R741NCIukYQmkmkCS05m1sHMOlU+B44GlgYVj4THb37zG+688046d+6c0nJPG7lXk7ZD/UmmclTdpWMHk5MdqbGvsRFxiSQ0kUwTZMupJ/Ba7Gbet4Bn3f35AOORAO3YsYPHH38cgEGDBnHKKaekvI7aMyxEzPhRA3PZAXUmn+qj6iYO68NNxx9YYzh3Y9MDJZLQRDJNXHPrhYXm1mu97rjjDi655BLefffdwJa9qE86hmNriHfGi2uETys956Vu4tewaKU/KCE6NdFLL73EmDFjgg5FpDkoOTUi6NF6ksHKysr4zW9+Q0lJCZFIRIlJRKooOUlgCgsL+f3vf8+sWbOCDkVEQibom3Alg40YMYIPP/ywWRYKFJGWRS0naVbl5eWce+65zJ07F0CJSUTqpOQkzWrr1q0sWLCAd955J+hQRCTE1K0nzaKiIjp3XF5eHm+88QY5ObrhVETqp5aTpJ27c8EFF3DuuedSUVGhxCQijVLLSZpF9+7dKSsrS8ns4iLS+ik5Sdq4O5s2baJbt25cd911uLuSk4jEJfBuPTOLmFmhmelml1bm6quvZvjw4WzYEF3oWIlJROIVhpbTxcAHQGqnoJbAjR8/nrKyMvbYY4+gQxGRFibQlpOZ9QV+APw5yDgkddydwsJCAAoKCrjpppvUYhKRJgu6W28a8P+AeteoNrPJZrbQzBYWFxc3X2SSkIcffpjhw4fz6quvBh2KSIukc15UYN16ZnYssN7d3zGz0fUd5+73APdAdIbeZgpPEnTiiSeyceNGRo0aFXQoIi2SznlRQbacRgHjzWwl8DfgSDN7OMB4JAmPP/44X3/9NTk5OVx88cVkZQXdKBeRliywlpO7XwFcARBrOf3K3X8UVDzSuKtmLuGRN1dT/V+5nOwsNq9bwWf3/w95o8+iy8gTqlaKbWiFWRGRhoRhtJ60AFfNXMLDb67ebXtpWQVt8wfQ49Trab/XAQA4VB2rBCUiiQhF34u7z3f3Y4OOQ+r36II1u23buuh5dny+HICc/gdhWZFG3yMi8du4bSfTF+z+T2EmCEVykvAr95rXZSt2fs3mN59g6zvPxP0eEZF4qVtP4hIxq5Fsstq2p9ekm4nkdmnwPSIiiVDLSeJy2si9APjqvRcoee0R3J02nbpjkexG3yMi0lRqOUlcKgc23PH8MnZtXg8V5RCp/9fnRxqtJyJJUHKSuJSXl3P9xAO5dvxz7Ny5k/bt2wMws7CIqXOWsa6klN55OVw6djATh/UJOFoRaemUnKRRjz32GFOnTmXOnDnsscceVYkJYOKwPkpGImnSrUNbTh/ZL+gwAqFrTtKovLy83ZKSiEg6KTlJvb788ksAxo4dy/PPP0+HDh0CjkhEMoWSk9Rp3rx5DBgwgPnz5wNaKFBEmpeSk9Tp4IMP5rTTTmPYsGFBhyIiGSjIJTPaA68A7WJxPOnuVwcVT6abWVjENf94n/WrlpHdvX90KqJuE3jhptcYtU83Hjnn0KBDFJEMEmTLaQdwpLsfBAwFxpnZIQHGk7FmFhZx6ROLKf5sDZ/99Zdsfv2xGvv/9clGJt37RkDRiWSuyrn1Kh+ZJMglMxz4KvYyO/bQZGwBmDpnGWUVTnZeL7qNOZfcwYftdsy/PtkI/KeFVVJaBkDX3Gyu/uG3mDisj+55EpGUMQ9wck4ziwDvAIOAP7j7ZXUcMxmYDNCvX7/hq1atat4gM8CeP/o9WR26kd11zwaPy8vJZsvXZVTE+SuTHTGmnniQEpTI7uodYVT9nNe9V5/hd8x8vWpfK7nnKa7RVYEOiHD3cncfCvQFRpjZAXUcc4+7F7h7QX5+fvMH2crt3LmTTc/exsYX/tjosSWl8ScmgLJy5xePL2JmYREQbXWNmvIie1/+LKOmvFi1XUT+o/o5r1Net6DDCUwoZohw9xIzmw+MA5YGHE5Gadu2LTf/38NMe+3ztPSpVjhc8tgiLnlsUY3tRSWlXPH0Ehau2shLHxarK1BEagis5WRm+WaWF3ueA4wBPgwqnkyzYMEC7rvvPgAuPnkMt5/9XfJy6p9hPB1Ky8p55M3VFJWU4vwnYalFJSJBtpz2BB6MXXfKAh5391kBxpNR7rzzThYsWMDpp59OTk5OnXPkjZryIkUlpWmNo3ZrrbSsnKlzlqn1JFKH6iP2Wsn1p3oFOVrvPUB3eAbk/vvvZ+PGjeTk5NR7zKVjB3PF00soLStvxshgXZoTooiEn2aIyCCLFy/mpJNOYtu2bbRr144992x4dN7CVRvTmpjqG7LTO6/+hCkimSEUAyIkfrXvJfrufvnMWvxZ1X1HEB3yfc34b+3WNfbBBx/w9ttvs2HDhkYncb1q5hIefjN9N/31icX+1DtFNRJgTnaES8cOTlu9ItIyKDm1ELVvfoXoAIK6EkhJaRmXPrEYiK63VF5eTiQS4dRTT2XChAkNduVVJr90XWuqvUJuQf9uunFXRHaj5NQCzCwsavK1n7IK55LHFnHBH59h/dM30P0HP6dd7/+0SMxg0shookh3QoJoS6muxKPFCkWkLkpOLcDUOcsSvvZj2e2J5HQmq11uje3u8PCbq1mw4kvWbvo67deW/nX5kWkrX0RaHyWnFiCRFk359s1k5XSmTed8ek66ud71mD5evy3Z8BqlCRNFUi8VE8GGeTi6Ruu1AJEmLvRXvm0Tnz1wCZtf/xughQJFpOVRcmoByps4OW9Wbhc6fPM75O47Mk0RiYikl7r1WoC8nOwao/Tqs2tLMRbJJtIhj66jz0p/YHH60SHh7ToQkXBSy6kFiKdXzivKWf/k7yiecSNNWQYlnb8AZrsPHRcRiUeQy7TvBfwV6AVUAPe4+x1BxRNmJdsbbzVZVoRuYyZjbdrFfY2pT14On20uTfmIhT55ORqdJyJJCbJbbxfwS3d/18w6Ae+Y2Vx3/3eAMYVS77ycekfslX+1iZ3rV5AzcDjt+w1ptKzaLZkBlz+bsjgraW48kXAJ86i8+gQ58etnwGex51vN7AOgD5DxyamuKYrqm0po08t/ofTjBfQ5736y2jU8JRFQVU5lgoqYNXnARWM0N56IJCsU15zMbADRGcoX1LFvspktNLOFxcXFzR1as7tq5hJ+/tiiGmscNTTHXbcxP6PHydfGlZgqPbpgTdXz00bulUy4u8kCzY0nkoTq57ytJRuDDicwgScnM+sIPAVc4u5bau/PpGXaKydbbawdU166hU2v/BWvKCerXW6NaYniUb2ldP3EAxm1T2qWgs7JzuK2U4ZqOiKRJGiZ9qhAh5KbWTbRxPSIuz8dZCxBauoM4KXL32Lr2zPpMHgUbXvu0+T6at/U+8g5hyY0v54Bn075QZPrFxFpTJCj9Qy4D/jA3W8LKo6gJbI0RccDx9C+3xDadOmRUJ11deVVn4A13hVwdW1JRNIlyJbTKOAMYImZLYpt+7W7PxdgTM2u+vWfhlTs2MaGZ2+n63fOInuPvgknpnjuO7p07GB+8fgiKhroX8yOmK4tiYRISxyR15AgR+u9Rv2LoWaMeEfKlX+1iZ2fL6es5DOy9+ibUF3T4rweVHnMZU+9x45dFbvt75qbzdU/3H0xQxGRVNH0RSHnFeVYVoTsPfrS+5z/Iyu7XULlVE4hNGrKizWGqL/0YXGdC/1pnSURCZKSU4BmFhY1uL+i7GvWP3ENud84jM4F4xNKTJWL/AE1FiysPUS9qKSUK55ewsJVG+tNWCIizUXJKSAzC4v4ZWwp9fpYVoRIbh6RDnkJ1dE1N7tqGqFRU15sdEHB0rJyHqk2lL0yYQFKUCLSrAK/zylT/fLxRZTXM+KgomwHFTtLsUg23SdcRof9j0iojk3by6paZ/EOEa8dUWlZOVPnLEuofhGRRKnlFICrZi6hvJ5xEO7Ohr9PoWJnKT1PvQHLiiRV19Q5y5g4rE9S0xRprjyRcGptI/SqU3IKQEPDx82MDt86Ei/7GsuK0KFthG07G+6Oa0hRSSl7X/5sUhOP634mEWluSk4BqKsF4+VllG0som3+ADrs/+2q7Y0lpuwsKNt9tHfNshOKMionO6L7mUSk2emaU0hsmv8Anz98KeVfbWrS+xpLTMm66fgDNRhCRJqdWk5pNOneN/jXJ/HNKtx55Am07bkPkY5d0xxV/Prk5SgxiUgg1HJKg5mFRQy8/NlGE5NXlPPV0hdxd9p07EbHA5p/9diIGdNOGUpOds2BF+rOE5EgBT0r+f3AscB6dz8gyFhSZWZhEZc+sZh4etu2ffAKXz57G5GO3cgZMDTtsdXltJF7VbWOqi9wqJtvRcJv+oKmTRqdLukYNRh0t94DwF3AXwOOIyVmFhZxyWOLGj8wpsM3R0cTU/+D0hhV3QyYVG0SWE1XJCJhEmhycvdXYqvgtnjxLn3hXsHm1x6l47Dv0yaAxNRHrSIRaQGCbjk1yswmA5MB+vUL5w1nMwuL4l6TadfGdWxZOJOs3M50Hv7DNEdWU5+8nKrpjEQknKqf87r3ytx/IkM/IKIlLNPelOl9svfoS+///gOdDj42ruM7tI2Ql5OdaGhVDDTAQaQF0DLtUaFvObUEjU3v4+5smncv7frsR4f9j4hrocCVtZY/j3d12rpUXl9SV56ItBRKTinQOy+nwcThu3ay84tPsDbZcU/iOmrKizWuDV06dnCTBltU0sKAIpmnNcy5Z57gZKApqdzsUWA00B34Arja3e+r7/iCggJfuHBhM0UXv5mFRfzisUW7DR93d/AKLCuC79oJkWzMElv8d9Q+3dg7v2Pc17ZysrO46fghSkoi4RTXiWDg/kP8+gdmNbnwkCenuD570KP1Tguy/lSpTABXPP0epdXmEyp55UHKvlxL/oTLsTZtk6qj8obeaacM5XfPvM+m7WUA5OVkc814tYxEpHVRt16KVN4nNODyZ6u2RTp0o2LHdkhy2YtK//pkI4+cc6gSkYi0ekpOKWbAru2bieR2oXPBeNw94a48EZFMpeSUAiNvmMsXW3cCsOXtv7P5zcfZ88zbadOlhxKTiEgClJya4Hu3zefj9dsaPCZn4HB2bS0m0mmPlNc/ap/MvedBROIXljn3KiUyQCP0N+GGRWOJacdnHwPRm2y7HfnTpJdXr23UPt145JxDU1qmiEhYqeUUp4YS0/ZP3qb4yd+Rf/xV5O57SErqax8xPrzhmJSUJSLS0qjllAI5ex9M1zHnkjOwICXlKTGJSKZTyykJ25b9i5wBw8hql5vUJK4GWkNJRKQaJacE7dqyng3PTKXzf02k63fOSrgcAz6tNY+eiEimU3JKUJvOPeh56o2067VvUuX0zstJUUQiIq1H0Mu0jwPuACLAn919SpDxNGTaKUO55LFFfLX0Rdp06k77/kNo3/ebSZWZZVrGQkTq161D27DPk5c2gQ2IMLMI8Afg+8A3gdPMLLmzfRpNHNYHL9/FlreeZsvCmUmXl5udxW0nD9U1JhGROgTZchoBLHf3FQBm9jdgAvDvAGNqUFakDT1PvQHLbrfbvmmn1Ew0MwuLmDpnGetKSjXYQUSkiYJMTn2ANdVerwVG1j4oDMu0P/nkk7z88st8cscd7PPr2VRfZKS+AQ2VE8GKiDRFGM55YRBkcqpr0rndFpdy93uAeyC6nlO6g6pLYWEhhYWFfP311xpZJyJpFYZzXhgEmZzWAntVe90XWBdQLHXatWsXbdq04frrr2fHjh20b98+6JBERDJCkDNEvA3sa2Z7m1lb4FTgHwHGU8Nzzz3HkCFDWLt2LWamxCQi0owCazm5+y4zuxCYQ3Qo+f3u/n5Q8dS2xx570Lt3bzp27Bh0KCIiGSfoZdqfA54LMobaiouLyc/PZ+TIkcydO1frMYmIBEATv1bz1ltvMXDgQGbOjN7HpMQkIhIMJadqDjjgAM4880xGjRoVdCgiIhlNyQl477332LFjB7m5ufzhD38gPz8/6JBERDJaxien4uJivv3tb/PLX/4y6FBERCQm42clz8/P5+6772b06NFBhyIiIjEZm5wWLlxIdnY2Bx10EKeffnrQ4YiISDUZmZwqKir4yU9+QnZ2Nm+//bZG5YmIhExGJqesrCxmzJhBVlaWEpOISAhl1ICIJUuWcNtttwEwcOBABgwYEGxAIiJSp4xKTvfddx+33norGzduDDoUERFpQCDJycxOMrP3zazCzAqaq95bb72Vt956i27dujVXlSIikoCgWk5LgeOBV9Jd0UcffcT48ePZuHEjkUiEPn20AKCISNgFkpzc/QN3X9YcdX366acsWrSI9evXN0d1IiKSAqEfrZfsksVjx47lo48+0npMItIiaJn2qLS1nMzsn2a2tI7HhKaU4+73uHuBuxckOuedEpOItBSpOOe1BmlrObn7mHSVLSIirVtGDSUXEZGWIaih5MeZ2VrgUOBZM5sTRBwiIhJOgQyIcPcZwIwg6hYRkfBTt56IiISOkpOIiISOkpOIiISOkpOIiISOkpOIiISOkpOIiISOkpOIiISOkpOIiISOuXvQMcTNzIqBVQm8tTuwIQUhpKqcsJalmJq/LMXUvOWEpawN7j6usYPM7Pl4jmuNWlRySpSZLXT3pFfcTVU5YS1LMTV/WYqp5caU6rKkJnXriYhI6Cg5iYhI6GRKcronZOWEtSzF1PxlKabmLSfMZUk1GXHNSUREWpZMaTmJiEgLouQkIiKhkzHJycxOMrP3zazCzJo89NPMxpnZMjNbbmaXJxHH/Wa23syWJlpGrJy9zOwlM/sg9rkuTqKs9mb2lpktjpX1uyRji5hZoZnNSrKclWa2xMwWmdnCJMvKM7MnzezD2Pfs0ATKGByLpfKxxcwuSSKmn8e+30vN7FEza59gORfHyni/qfHU9ftoZt3MbK6ZfRz72jWJspr8d1dPOVNjP7v3zGyGmeUlUdZ1sXIWmdkLZtY7kXKq7fuVmbmZdY8nJomTu2fEA9gfGAzMBwqa+N4I8AkwEGgLLAa+mWAcRwAHA0uT/Dx7AgfHnncCPkoiJgM6xp5nAwuAQ5KI7RfAdGBWkp9xJdA9RT//B4Gfxp63BfKSLC8CfA70T/D9fYBPgZzY68eBsxIo5wBgKZBLdGXrfwL7NuH9u/0+Ar8HLo89vxy4OYmymvx3V085RwNtYs9vTjKmztWeXwT8KZFyYtv3AuYQnRwgJb+rekQfGdNycvcP3H1Zgm8fASx39xXuvhP4GzAhwTheATYmGEf1cj5z93djz7cCHxA94SVSlrv7V7GX2bFHQiNlzKwv8APgz4m8Px3MrDPRk8t9AO6+091Lkiz2KOATd09kxpJKbYAcM2tDNLmsS6CM/YE33X27u+8CXgaOi/fN9fw+TiCazIl9nZhoWYn83dVTzguxzwfwJtA3ibK2VHvZgTh+1xv4u70d+H/xlCFNkzHJKUl9gDXVXq8lwUSQDmY2ABhGtMWTaBkRM1sErAfmunuiZU0j+sdakWgs1Tjwgpm9Y2aTkyhnIFAM/CXW3fhnM+uQZGynAo8m+mZ3LwJuAVYDnwGb3f2FBIpaChxhZnuYWS5wDNH/5pPR090/i8X5GdAjyfJS7b+B2ckUYGY3mNkaYBLw2wTLGA8UufviZGKRurWq5GRm/4z1vdd+JNTKqV50HdtC8Z+SmXUEngIuqfUfYZO4e7m7DyX6H+kIMzsggViOBda7+zuJxlHLKHc/GPg+cIGZHZFgOW2Idsnc7e7DgG1Eu6sSYmZtgfHAE0mU0ZVoC2VvoDfQwcx+1NRy3P0Dot1cc4HniXY572rwTS2YmV1J9PM9kkw57n6lu+8VK+fCBOLIBa4kwcQmjWtVycndx7j7AXU8/p5k0Wup+d9oXxLrgkkpM8smmpgecfenU1FmrLtrPpDIZJOjgPFmtpJo1+eRZvZwErGsi31dD8wg2r2aiLXA2mqtwSeJJqtEfR94192/SKKMMcCn7l7s7mXA08BhiRTk7ve5+8HufgTRrqePk4gL4Asz2xMg9nV9kuWlhJn9GDgWmOTuqfrncDpwQgLv24foPxaLY7/vfYF3zaxXiuLKeK0qOaXR28C+ZrZ37L/mU4F/BBmQmRnRaygfuPttSZaVXzn6ycxyiJ44P2xqOe5+hbv3dfcBRL9HL7p7k1sDsTg6mFmnyudEL4gnNMLR3T8H1pjZ4Nimo4B/J1JWzGkk0aUXsxo4xMxyYz/Lo4heN2wyM+sR+9oPOD4Fsf0D+HHs+Y+BZP+5S5qZjQMuA8a7+/Yky9q32svxJPa7vsTde7j7gNjv+1qiA5Q+TyY2qSboERnN9SB6kXgtsAP4ApjTxPcfQ3RE3CfAlUnE8SjRawxlsXh+kmA5hxPtWnwPWBR7HJNgWUOAwlhZS4HfpuD7PZokRusRvU60OPZ4P5nveay8ocDC2GecCXRNsJxc4EugSwq+R78jemJcCjwEtEuwnFeJJtvFwFHJ/j4CewDziLbA5gHdkiiryX939ZSznOh138rf9UZH2DVQ1lOx7/l7wDNAn0TKqbV/JRqtl9KHpi8SEZHQUbeeiIiEjpKTiIiEjpKTiIiEjpKTiIiEjpKTiIiEjpKTtDqxGcjPb4Z6RuB6XukAAAHbSURBVJtZQjfOikjDlJykNcoD4k5OFpXI38JoEpzVQUQapvucpNUxs8pZ45cBLxG9ybgr0dnWr3L3v8cmy50d238o0Zm3xxCdhWAd0RtQd7j7hWaWD/wJ6Ber4hKgiOjs2OVEJ5X9H3d/tTk+n0gmUHKSVieWeGa5+wGVy1G4+5bYYnBvAvsC/YEVwGHu/mZswbnXic65txV4EVgcS07TgT+6+2uxKYLmuPv+ZnYN8JW739Lcn1GktWsTdAAiaWbAjbEZzSuILnXSM7Zvlbu/GXs+AnjZ3TcCmNkTwDdi+8YA34xOgQdA58p5/0QkPZScpLWbBOQDw929LDaDdOVy6NuqHVfXsiiVsoBD3b20+sZqyUpEUkwDIqQ12kp06XqALkTXmCozs+8S7c6ry1vAd8ysa6wrsPoyCi9Qbc0fMxtaRz0ikkJKTtLquPuXwL/MbCnR2cgLzGwh0VZUncsjeHRl2huJrib8T6KzfG+O7b4oVsZ7ZvZv4Gex7c8Ax5nZIjP7dto+kEgG0oAIkRgz6+juX8VaTjOA+919RtBxiWQitZxE/uMaM1tEdK2fT4mu+yQiAVDLSUREQkctJxERCR0lJxERCR0lJxERCR0lJxERCR0lJxERCZ3/D0vrAo1ro1JYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = df[(df.interchange==0) & (df.unroll == 0) & (df.tile == 0)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange==0) & (df.unroll == 0) & (df.tile == 1)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange==0) & (df.unroll == 1) & (df.tile == 0)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange==1) & (df.unroll == 0) & (df.tile == 0)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange==0) & (df.unroll == 1) & (df.tile == 1)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange==1) & (df.unroll == 1) & (df.tile == 0)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange==1) & (df.unroll == 0) & (df.tile == 1)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange==0) & (df.unroll == 1) & (df.tile == 1)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange==1) & (df.unroll == 1) & (df.tile == 1)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df1 = df[(df.interchange + df.tile + df.unroll != 0)]\n",
    "joint_plot(df1, f\"Validation dataset, {loss_func} loss\")\n",
    "df2 = df\n",
    "joint_plot(df2, f\"Validation dataset, {loss_func} loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
